{"access":{"advertiser_pricing_url":"https://aidevboard.com/pricing","catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. 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We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there.  \n A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. \n Why Safety AI Systems? \n As Senior Engineering Manager for Safety AI Systems at Roblox, you'll lead technical efforts and manage a team of experienced engineers to develop innovative AI solutions for multimodal content safety. You’ll oversee machine learning systems, constructing multimodal model architectures, improving data quality, training pipelines, and model performance to address challenges like real-time multi-verse content understanding and advanced moderation with large vision language models, spanning avatars, images, videos, audios, text, code / data models, and their composites. \n In close collaboration with product, policy, and Trust \u0026 Safety teams, you'll design large-scale systems to detect and mitigate abusive behavior before it harms the community. You'll own critical services at massive scale, balancing user freedom with platform civility to protect and empower our users. Your leadership will help ensure Roblox remains a safe, inclusive space for self-expression and shared experiences.\n You Will \n \n Own the vision, technical direction, and execution of machine learning solutions for the Multimodal Safety AI system, ensuring these systems effectively detect and prevent harmful content at scale.\n Lead and grow a high-performing team of ML engineers, fostering a culture of innovation, technical excellence, accountability, and inclusivity, while mentoring and developing talent.\n Break down ambitious long-term goals into an actionable, iterative roadmap – delivering continuous improvements in stages and driving tangible value at each step.\n Architect and guide the development of large-scale machine learning models with innovative architectures, ensuring they achieve high quality and are production-ready.\n Drive alignment on complex technical decisions across multiple teams (within and beyond the Safety org), demonstrating empathy and building consensus among diverse stakeholders.\n Collaborate cross-functionally with Product, Data Science, Policy, Design, and Operations partners to define and prioritize the machine learning roadmap for multimodal safety initiatives, ensuring alignment with broader Safety and Roblox objectives.\n Stay ahead of emerging trends in AI/ML and content moderation techniques, continuously innovating our safety approaches to anticipate new challenges. \n \n You Have \n \n 5+ years of experience building large-scale machine learning systems in production environments.\n Proven track record of designing, developing, and launching ML models from scratch into production.\n 2+ years of hands-on experience with vision language models or other foundation model technologies.\n Expertise in solving complex ML modeling, data, and infrastructure challenges – with a focus on maintaining high quality and velocity at scale.\n Ability to thrive in ambiguity: you excel at bringing clarity and direction to undefined or open-ended problem spaces.\n Demonstrated success collaborating across functions (e.g. Product, Design, Data, Research), working together to drive meaningful business and user impact.\n Strong product sense: able to establish clear success metrics and craft strategic roadmaps to achieve those goals.\n High emotional intelligence: adept at resolving conflicts, mentoring engineers, and nurturing the growth of your team members.\n Experience with modern microservice architectures and distributed systems programming paradigms (e.g. cloud services, scalable data pipelines).\n Hands-on to dive into code/architecture and guide technical discussions when needed, in addition to high-level planning. \n \n You Are \n \n Creative and strategic problem-solver: able to distill complex issues into simple, innovative solutions that drive impact.\n An owner: you take responsibility for projects and outcomes end-to-end, and instill the same accountability in your team.\n A lifelong learner: constantly seeking new knowledge and techniques to grow your expertise and expand your impact.\n Independent and self-directed: capable of charting a course with minimal guidance, and comfortable making decisions in uncertain situations.\n Highly collaborative: effective at working with cross-functional partners and teams, and skilled a","salary_min":295250,"salary_max":345040,"location":"San Mateo, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["data-pipeline","generative-ai","microservices","distributed-systems","machine-learning"],"apply_url":"https://careers.roblox.com/jobs/8047877?gh_jid=8047877","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-16T02:50:02Z","expires_at":"2026-08-15T14:18:31.254864Z","created_at":"2026-07-16T14:18:31.377819Z","updated_at":"2026-07-16T14:18:31.377819Z","company_name":"Roblox","company_slug":"roblox","company_logo_url":"https://www.google.com/s2/favicons?domain=roblox.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/acccc186-1fa0-414d-bb62-82bf62875fe7"},{"id":"9313b027-a169-43a5-b77c-bcd6097769df","company_id":"332b7698-676b-4a3e-8b02-81b1195c5af6","title":"Senior Software Engineer, Foundation Model API","slug":"senior-software-engineer-foundation-model-api-c724567b","description":"P-1428 \n At Databricks, we are passionate about enabling data and AI teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers — and customer-obsessed — we leap at every opportunity to solve technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started.\n As part of the AI team, you'll build the platforms and products that power everything from data apps, AI agents, model training, model serving, and Vector Search. You'll be joining a high-agency, high-visibility team operating at the frontier of AI infrastructure — with deep ties to research, product, and real-world enterprise use cases. Databricks Mosaic AI is one of our fastest-growing businesses, helping thousands of our customers democratize AI within their organizations. We're building the products and infrastructure that power the next generation of AI.\n We're hiring across multiple teams in our AI Engineering org, including the FMAPI (Foundation Model APIs) team — the unified serving layer for large language models across real-time and batch inference, powering model inference at enterprise scale. We are looking to hire high-agency L6 Engineers who bridge the gap between technical execution and product strategy.\n The impact you will have: \n \n Build LLM infrastructure powering large-scale inference workloads for customers through partner models (OpenAI, Anthropic, Gemini) and self-hosted models (Qwen, GPT-OSS, Llama)\n Shape the direction of the FMAPI product — from roadmap to execution — by leveraging deep customer empathy and direct engagement with enterprise users and model providers\n Improve reliability, latency, and efficiency of distributed AI workloads\n Collaborate with platform, infra, and ML teams to deliver seamless end-to-end experiences\n Shape how developers and data scientists build and interact with AI on Databricks\n \n What we look for: \n \n 8+ years of experience in backend or infrastructure engineering\n Experience with distributed systems, scalable APIs, or cloud-native infrastructure\n Strong product and ownership mindset, with a focus on shipping user-facing value\n Experience with real-time serving, ML infrastructure, or GPU orchestration\n Familiarity with service-oriented architecture, deployment pipelines, and system observability\n Strong programming skills in Scala, Go, or Python\n \n Bonus points for: \n \n Exposure to platforms like SageMaker, Vertex AI, or Azure ML\n Built products that support AI workflows\n  \n Pay Range Transparency \n Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles.  Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here . \n  \n Local Pay Range\n $160,000 — $225,000 USD \n About Databricks \n Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on  Twitter ,  LinkedIn   and   Facebook . Benefits At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here . \n Our Commitment to Diversity and Inclusion \n At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, soc","salary_min":160000,"salary_max":225000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["distributed-systems","llm","generative-ai","agents","cloud","mlops","data-pipeline"],"apply_url":"https://databricks.com/company/careers/open-positions/job?gh_jid=8635900002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-15T21:49:06Z","expires_at":"2026-08-15T14:02:30.870498Z","created_at":"2026-07-16T14:02:30.99446Z","updated_at":"2026-07-16T14:02:30.99446Z","company_name":"Databricks","company_slug":"databricks","company_logo_url":"https://www.google.com/s2/favicons?domain=databricks.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9313b027-a169-43a5-b77c-bcd6097769df"},{"id":"b2503a2d-d800-43bc-9f84-11af04a6a4b4","company_id":"77beb456-fc80-40a4-b773-f0b17d1ece4c","title":"Generative AI - Graphics Engineer","slug":"generative-ai-graphics-engineer-429a27ce","description":"WHO YOU ARE\n\nWe are looking for skilled graphics engineer who have a deep command of modern C++ and GPU programming, a strong mathematical foundation, and an expert understanding of computer graphics—whether rendering, geometry processing, simulation, or advanced real-time techniques. You collaborate naturally with artists, researchers, and engineers, explaining complex ideas with clarity and learning from diverse perspectives. You’re not afraid of new ideas or unfamiliar pipelines. Most importantly, you’re excited to build the next generation of 3D creation technology—graphics systems that will empower millions of creators worldwide.\n\n\nWHO WE ARE\n\nAt Meshy, we believe 3D creation should be boundless and accessible. Our mission statement is simple: unleash creativity. We built a full pipeline for 3D content ranging from text / image to 3D, texturing, texture editing, animation rigging, etc. We also built a vibrant community for our creators, where people can share their work, take inspiration from others, and even use it as an asset marketplace for their games and prototypes. We are the market leader in 3D generative AI, recognized as the No.1 in popularity among 3D AI tools (according to 2024 A16Z Games survey), and we generate real value and is used by enterprises (including Meta, Square Enix, Deepmind, etc.) and millions of end users. Meshy is used in game and film production, in 3D printing, in industrial product design, in enablement of novel product features such as user-generated content, and even in training and simulation for robotics and physical AI.\n\n\nYOUR NEXT CHALLENGE\n\nAs a core member of Meshy’s algorithm team, you will design and build the next generation of high-performance graphics systems that power our 3D generative AI training and products. You will collaborate closely with graphics experts, generative AI researchers, and infrastructure engineers to enable new creative capabilities and push the boundaries of what AI-empowered 3D pipelines can achieve.\n\n \n\nIn this role, you will:\n\n - Build and optimize high-performance graphics components—rendering kernels, geometry processing operators, and supporting systems.\n\n - Develop robust production-quality pipelines that integrate with data pipelines, generative models and artist-facing applications.\n\n - Work across GPU clusters, cloud environments, and local DCC tools to ensure seamless interoperability and scalability.\n\n - Collaborate closely with artists, product teams, and ML researchers to translate creative requirements into technical implementations.\n\n - Contribute to internal tooling, demos, documentation, open-source initiatives, or technical reports that elevate Meshy’s graphics capabilities.\n\n\nWHAT WE'RE LOOKING FOR\n\n - Expert-level C++ and GPU programming skills, with a strong ability to write high-performance, memory-efficient code.\n\n - Solid mathematical foundation with deep understanding of computer graphics—either rendering, geometry processing, or both.\n\n - Hands-on experience building production-grade graphics systems, such as rendering engines, geometry pipelines, asset tools, or similar large-scale systems.\n\n - Strong engineering discipline—clean code, reproducible results, rigorous profiling, and sustainable system design.\n\n - Working knowledge of major DCC tools (Houdini, Blender, Maya), including experience developing scripts, plug-ins, or custom tools within these environments is a plus.\n\n - Experience in AAA game development, VFX pipelines, or other high-end 3D production environments is a plus.\n\n - Demonstrated contributions to open-source graphics projects or publications in top-tier CG venues (SIGGRAPH, etc.) are pluses.\n\n\nA LITTLE MORE ABOUT MESHY.AI\n\nTrusted by Meta, Square Enix, Deepmind and more, Meshy is redefining 3D creation with generative AI. We empower artists, designers, engineers, hobbyists, and makers to bring immersive worlds, characters, and experiences to reality in minutes instead of months.\n\n \n\nIn addition to our core mission of unleashing creativity, we build a culture that we enjoy and are proud of. Here are some highlights:\n\n - We value intelligence and the pursuit of knowledge. We are a global team of generative-AI pioneers, computer-graphics veterans, and product builders who believe human expression and enjoyment is the ultimate frontier of computing.\n\n - We care deeply about our work, our users, and each other. Empathy and passion drive us forward. We have a culture of directness and truthfulness, therefore we value constructive criticism. Being direct and truthful is the most sincere form of trust and care.\n\n - We trust our instincts and are not afraid to take bold risks. Meshy was born from a few-hour prototype, a bold pivot for a team that had very little experience in AI. Innovation requires courage.\n\n - We have a keen eye for quality and aesthetics. Our products are not just functional but also beautiful. The same aesthetics permeate through our culture, our code and ","salary_min":175000,"salary_max":300000,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["gpu","generative-ai","data-pipeline","robotics","computer-graphics","research"],"apply_url":"https://jobs.ashbyhq.com/meshy/e08ff336-379d-4cde-8df0-c5ab335517b3/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-15T20:04:34.461Z","expires_at":"2026-08-15T14:10:57.040052Z","created_at":"2026-07-16T14:10:57.180092Z","updated_at":"2026-07-16T14:10:57.180092Z","company_name":"Meshy","company_slug":"meshy","company_logo_url":"https://www.google.com/s2/favicons?domain=meshy.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/b2503a2d-d800-43bc-9f84-11af04a6a4b4"},{"id":"c86d2f91-914b-4899-b3e1-e61be0732f6a","company_id":"77beb456-fc80-40a4-b773-f0b17d1ece4c","title":"Generative AI - ML System Engineering","slug":"generative-ai-ml-system-engineering-b7b36ecf","description":"WHO YOU ARE\n\nWe are looking for Machine Learning Systems Engineers who can help us build the world's largest end-to-end 3D native machine learning systems. You will help us build our end to end ML framework dedicated for 3D, from pretraining, to finetuning, inferencing, etc. We expect a combination of strong hands on engineering skills, eagerness to learn new things, and thrives in a fast-paced, high-ownership environment.\n\n\nWHO WE ARE\n\nAt Meshy, we believe 3D creation should be boundless and accessible. Our mission statement is simple: unleash creativity. We built a full pipeline for 3D content ranging from text / image to 3D, texturing, texture editing, animation rigging, etc. We also built a vibrant community for our creators, where people can share their work, take inspiration from others, and even use it as an asset marketplace for their games and prototypes. We are the market leader in 3D generative AI, recognized as the No.1 in popularity among 3D AI tools (according to 2024 A16Z Games survey), and we generate real value and is used by enterprises (including Meta, Square Enix, Deepmind, etc.) and millions of end users. Meshy is used in game and film production, in 3D printing, in industrial product design, in enablement of novel product features such as user-generated content, and even in training and simulation for robotics and physical AI.\n\n\nYOUR NEXT CHALLENGE\n\n3D is the brave new frontier of Gen AI. Our work here involves a lot of unique new challenges in both training and inference. Your next challenge at Meshy would involve the full stack of AI, from debugging and monitoring the hardware platform, building training framework, scaling high-throughput 3D data pipelines for our foundational training, co-designing novel model architectures with researchers, to the novel challenge of efficient inference engines for diffusion models and more. Here are some examples for each side of the challenge:\n\n \n\nOn the training side\n\n - Work closely with researchers to co-design the next frontier of 3D \u0026 Spatial AI.\n\n - Build and debug on top of modern PyTorch, for maximum parallelism and efficiency, and build clean and intuitive training infrastructure for our in-house foundational models.\n\n - Identifying bottlenecks and optimizing for high throughput \u0026 efficient distributed model training across hundreds to thousands of GPUs.\n\n - Implementing and maintaining 3D specific custom operators in Triton or CUDA.\n\n - Implementing and maintaining novel data-loading framework and libraries.\n\nOn the inference side\n\n - Building efficient inference endpoints with complex multi-stage model pipelines.\n\n - Optimizing models through compilation, fusion, quantization, etc.\n\n\nWHAT WE'RE LOOKING FOR\n\n - Experience in machine learning or high performance graphics.\n\n - Solid practical understanding of at least one machine learning framework (e.g. PyTorch, JAX).\n\n - Strong ability to write beautiful and maintainable code in Python and/or C++.\n\n - Ability to learn fast and dive into new concepts or complex codebases.\n\n - Performance and efficiency oriented mindset, with a strong interest in the tiniest detail.\n\n - Strong communication skills for working in a globally distributed team.\n\n\nNICE TO HAVE\n\n - A strong passion to navigate through the PyTorch internals, with hands-on experience in areas like torch.compile , fully_shard (FSDP2) APIs.\n\n - Experience with building Triton kernels.\n\n - Experiences with large-scale distributed training, familiarity with modern parallelization techniques: DP, TP, CP, PP, zero redundancy optimizers, etc.\n\n - Experience with diffusion models in 3D or video.\n\n - Experience with low precision bf16 or fp8 training.\n\n\nA LITTLE MORE ABOUT MESHY.AI\n\nTrusted by Meta, Square Enix, Deepmind and more, Meshy is redefining 3D creation with generative AI. We empower artists, designers, engineers, hobbyists, and makers to bring immersive worlds, characters, and experiences to reality in minutes instead of months.\n\n \n\nIn addition to our core mission of unleashing creativity, we build a culture that we enjoy and are proud of. Here are some highlights:\n\n - We value intelligence and the pursuit of knowledge. We are a global team of generative-AI pioneers, computer-graphics veterans, and product builders who believe human expression and enjoyment is the ultimate frontier of computing.\n\n - We care deeply about our work, our users, and each other. Empathy and passion drive us forward. We have a culture of directness and truthfulness, therefore we value constructive criticism. Being direct and truthful is the most sincere form of trust and care.\n\n - We trust our instincts and are not afraid to take bold risks. Meshy was born from a few-hour prototype, a bold pivot for a team that had very little experience in AI. Innovation requires courage.\n\n - We have a keen eye for quality and aesthetics. Our products are not just functional but also beautiful. The same aesthetics permeate through our culture, our code and are the ","salary_min":175000,"salary_max":300000,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["fine-tuning","pytorch","distributed-systems","pre-training","generative-ai","gpu","robotics","data-pipeline"],"apply_url":"https://jobs.ashbyhq.com/meshy/3f94dcd6-9d31-47e7-a6b1-66e49a777056/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-15T19:52:38.747Z","expires_at":"2026-08-15T14:10:55.956272Z","created_at":"2026-07-16T14:10:56.084849Z","updated_at":"2026-07-16T14:10:56.084849Z","company_name":"Meshy","company_slug":"meshy","company_logo_url":"https://www.google.com/s2/favicons?domain=meshy.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/c86d2f91-914b-4899-b3e1-e61be0732f6a"},{"id":"6db6f99f-a30e-4524-a8e4-b34154992b4d","company_id":"77beb456-fc80-40a4-b773-f0b17d1ece4c","title":"Generative AI - 3D Foundation Model","slug":"generative-ai-3d-foundation-model-cb2667cc","description":"WHO YOU ARE\n\nYou are a talented, hands-on researcher who thrives in a fast-paced environment, is self-directed, a team player, and knows how to get things done efficiently. You have deep understanding of the transformer architecture, have strong python and tensor programming skills, have a vision for AI beyond linear sequences, and you believe in \"the scaling law\". You can translate high-level goals into concrete research and implementation steps, set an approach, and follow through. When it's time to explain your ideas, you bring clarity to complex technical issues. You are not afraid of confronting new ideas, and you are eager to share your knowledge with the team. You use these skills to create real-world benefits for our researchers, engineers, and millions of users, and you are excited to help advance our effort to push the state of the art of AI that understands and generates 3D worlds.\n\n\nWHO WE ARE\n\nAt Meshy, we believe 3D creation should be boundless and accessible. Our mission statement is simple: unleash creativity. We built a full pipeline for 3D content ranging from text / image to 3D, texturing, texture editing, animation rigging, etc. We also built a vibrant community for our creators, where people can share their work, take inspiration from others, and even use it as an asset marketplace for their games and prototypes. We are the market leader in 3D generative AI, recognized as the No.1 in popularity among 3D AI tools (according to 2024 A16Z Games survey), and we generate real value and is used by enterprises (including Meta, Square Enix, Deepmind, etc.) and millions of end users. Meshy is used in game and film production, in 3D printing, in industrial product design, in enablement of novel product features such as user-generated content, and even in training and simulation for robotics and physical AI.\n\n\nYOUR NEXT CHALLENGE\n\nAs a core member of the team of research scientists and machine learning engineers at Meshy, you will drive the development of our core 3D-native generative foundational model. In this role, you will join our foundational research to advance 3D AI, apply learnings from other fields of ML, and pushing the state of the art. You will also work towards long-term ambitious research goals, while identifying intermediate milestones.\n\n \n\nThe essential functions include, but are not limited to the following:\n\n - Design, train, and refine large-scale 3D generative models from covering pre-training, post-training, and emerging paradigms in diffusion, flow matching, and multi-modal learning.\n\n - Bridge the gap between cutting-edge research and product, deploy models in real products used by millions of creators, using human feedback and creative evaluation.\n\n - Create novel model architectures to make 3D generation faster, higher-quality, and more controllable.\n\n - Collaborate with infrastructure and systems teams to build scalable training, and data pipelines across GPU clusters and cloud environments.\n\n - Bring engineering discipline into an fast-paced research environment: elegant code, reproducible experiments, and building software as a team.\n\n - Share insights and breakthroughs through internal demos, open-source contributions, or technical reports that advance the field of 3D generative AI.\n\n\nWHAT WE'RE LOOKING FOR\n\n - Strong engineering skills in Python and deep learning frameworks (preferably PyTorch); comfortable moving between research prototypes and production systems.\n\n - Familiar with Transformers and modern generative AI models (Diffusion / flow matching, VAE, etc.).\n\n - Curiosity and passion for multi-modal AI, and have an intuitive understanding of how models perceive, represent, and generate 3D worlds.\n\n - Familiar with high performance training on large scale infrastructure (e.g., SLURM, Ray, k8s) is a plus.\n\n - Contributions to popular open-source machine learning projects or publications in top-tier CV / ML conferences is a plus.\n\n\nA LITTLE MORE ABOUT MESHY.AI\n\nTrusted by Meta, Square Enix, Deepmind and more, Meshy is redefining 3D creation with generative AI. We empower artists, designers, engineers, hobbyists, and makers to bring immersive worlds, characters, and experiences to reality in minutes instead of months.\n\n \n\nIn addition to our core mission of unleashing creativity, we build a culture that we enjoy and are proud of. Here are some highlights:\n\n - We value intelligence and the pursuit of knowledge. We are a global team of generative-AI pioneers, computer-graphics veterans, and product builders who believe human expression and enjoyment is the ultimate frontier of computing.\n\n - We care deeply about our work, our users, and each other. Empathy and passion drive us forward. We have a culture of directness and truthfulness, therefore we value constructive criticism. Being direct and truthful is the most sincere form of trust and care.\n\n - We trust our instincts and are not afraid to take bold risks. Meshy was born from a few-hour prototype, a bold pivot","salary_min":175000,"salary_max":300000,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["generative-ai","gpu","robotics","deep-learning","data-pipeline","pre-training","pytorch","research"],"apply_url":"https://jobs.ashbyhq.com/meshy/f52aa172-0212-4db8-a93d-406b910b9fea/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-15T19:18:36.925Z","expires_at":"2026-08-15T14:10:56.873764Z","created_at":"2026-07-16T14:10:56.993732Z","updated_at":"2026-07-16T14:10:56.993732Z","company_name":"Meshy","company_slug":"meshy","company_logo_url":"https://www.google.com/s2/favicons?domain=meshy.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/6db6f99f-a30e-4524-a8e4-b34154992b4d"},{"id":"7466ea68-e22a-4989-93a5-1db0ae5979e1","company_id":"a0000000-0000-0000-0000-000000000003","title":"Software Engineering Manager, Public Sector ","slug":"software-engineering-manager-public-sector-dd16fefc","description":"Scale AI’s Public Sector business is growing quickly as government agencies adopt AI to support critical national security, defense, and public sector missions. We’re looking for a hands-on Engineering Manager to lead a team of software engineers building core products and infrastructure for these customers.\n This role is ideal for someone who thrives in technical environments, enjoys managing teams while staying close to the code, and wants to work on meaningful problems that impact real world operations across the U.S. government. You’ll play a critical role in delivering backend systems, distributed platforms, and ML tooling used by our public sector partners—all while helping your team grow and execute.\n You’ll split your time between technical planning and execution (50%) and people management and team development (50%) , leading a team of 6-8 engineers. You’ll work cross-functionally with product, security, and customer-facing teams to ensure our engineering efforts meet complex federal compliance, security, and performance needs.\n Must be able to commute to office three times per week \n You will: \n \n Recruit a high-performing engineering team. \n Drive engineering productivity. Provide guidance, mentorship, and technical leadership to a team of engineers working on Generative AI projects. \n Collaborating with cross-functional teams to define, design, and execute strategic roadmap.\n Navigate and deliver outcomes while navigating through complex public sector compliance requirements and frameworks.\n Design and implement scalable backend systems for Federal customers, leveraging Scale's modern and cloud-native AI infrastructure\n Develop distributed systems, data-intensive applications, and machine learning infrastructure to enable real impact for mission owners\n Build robust and reliable backend systems that can serve as standalone products, empowering customers to accelerate their own AI ambitions\n Participate actively in customer engagements, working closely with stakeholders to understand requirements and deliver innovative solutions\n Contribute to the platform roadmap and product strategy for Scale AI's Federal business, playing a key role in shaping the future direction of our offerings\n Have or ability to obtain a TS/SCI clearance \n \n Ideally you’d have: \n \n 5+ years of full-time engineering experience, post-graduation\n 2+ years of prior engineering management or equivalent experience and has managed an engineering team.\n Have extensive experience in software development\n Experience scaling products at hyper-growth startups\n Excitement to work with AI technologies and their applications for the public sector\n Extremely strong track record as an individual contributor\n Show a track record of mentoring and leading teams in successful projects\n Possess excellent communication and collaboration skills, and the ability to translate complex technical concepts to non-technical stakeholders\n \n Nice to haves: \n \n TS/SCI Clearance\n Deep technical knowledge of Software Development, willing to get deep into the weeds to solve problems alongside the team.\n Have experience with AI platforms and technologies, including generative models and LLMs.\n Have previous experience in government or government facing technology roles\n Experience with cloud-native technologies, full stack development, data engineering, and ml ops infrastructure\n Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend. \n Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York is:\n $216,000 — $270,000 USD \n Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of Washington DC is:\n $194,400 — $243,000 USD \n Please reference the job posting's subtitle for wher","salary_min":162400,"salary_max":203000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["mlops","distributed-systems","llm","generative-ai"],"apply_url":"https://job-boards.greenhouse.io/scaleai/jobs/4715325005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-15T01:05:55Z","expires_at":"2026-08-15T14:01:45.42932Z","created_at":"2026-07-15T14:01:49.182522Z","updated_at":"2026-07-16T14:01:45.554313Z","company_name":"Scale AI","company_slug":"scale-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=scale.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/7466ea68-e22a-4989-93a5-1db0ae5979e1"},{"id":"be679834-9c8e-4780-bdad-f2d02b24a22e","company_id":"a0000000-0000-0000-0000-000000000001","title":"Safeguards Enforcement Analyst, Violence \u0026 Extremism","slug":"safeguards-enforcement-analyst-violence-extremism-5a4dffe7","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the role \n As a Safeguards Enforcement Analyst focused on Violence \u0026 Extremism, you will be responsible for building and executing operational workflows to assess model behavior, drive enforcement decisions, and develop evals across a technically demanding range of policy areas. Your work spans detecting and mitigating attempts to misuse Anthropic's AI systems to facilitate real-world harm, including weapons and dangerous technology, critical infrastructure attacks, violent extremism, and threats of violence.\n Important context for this role: In this position you may be exposed to and engage with explicit content spanning a range of topics, including those of a violent, graphic, hateful, or psychologically disturbing nature.\n Key responsibilities \n \n Design and architect automated enforcement systems and review workflows that scale effectively while maintaining high accuracy\n Develop and maintain evals that measure model performance on these policy areas, surface regressions, and inform policy and model improvements\n Partner with Engineering and Data Science to optimize detection and automated enforcement systems for potential policy violations\n Review flagged content to drive enforcement decisions and surface policy gaps, with particular attention to novel or technically sophisticated misuse attempts + emerging extremist movements, ideologies, and mobilization tactics\n Support the Safeguards policy design team by providing structured feedback on policy gaps and enforcement ambiguities based on real enforcement scenarios\n Develop and maintain enforcement guidelines and reviewer documentation that enable accurate, consistent enforcement across a wide range of content\n Keep up to date with emerging threats, terrorist and extremist movements, regulatory changes, and AI policy enforcement best practices, and apply these to inform our workflows and evals\n Identify and escalate emerging misuse patterns, novel attack vectors, and signs of coordinated violent extremist activity\n \n Minimum qualifications \n \n Experience in policy enforcement, threat intelligence, counterterrorism, government, or a closely related field, with direct exposure to harmful content, dangerous technology, violent extremism, or physical harm facilitation\n Experience standing up and scaling policy enforcement or content review workflows\n Proficiency in SQL and/or other data analysis tools to draw insights from large datasets and monitor enforcement workflow health\n Experience identifying emerging risks and threat actors, and communicating findings to a diverse set of stakeholders, such as Product, Policy, Engineering, and Legal teams\n Experience working with generative AI products, including writing effective prompts for content review and enforcement\n Understanding of the challenges involved in implementing product policies at scale, including in the content moderation space\n \n Preferred qualifications \n \n Subject matter expertise in one or more high-stakes harm areas, such as weapons and dangerous technology, violent extremism, terrorism, autonomous systems, or critical infrastructure protection\n Familiarity with relevant legal and regulatory frameworks governing dangerous technology, critical infrastructure, or domestic/international terrorism\n Experience developing evals or red-teaming AI systems, particularly for harmful content or policy enforcement use cases\n Experience with threat actor profiling and threat intelligence frameworks (e.g., MITRE ATT\u0026CK)\n Experience tracking threat actors, extremist networks, or misuse patterns across surface, deep, and dark web environments\n Experience with large language models and an understanding of how AI technology could provide meaningful uplift toward serious harm\n Proficiency in Python for data analysis and workflow automation\n Background in law enforcement, national security, defense, counterterrorism, or a relevant regulatory environment\n Experience assessing the technical plausibility and real-world harm potential of content, including the ability to distinguish between general educational content and genuine operational uplift, and between protected speech and genuine incitement/mobilization\n Familiarity with cross-platform threat analysis and OSINT techniques\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $285,000 — $330,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of ","salary_min":285000,"salary_max":330000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["alignment","llm","generative-ai","rust"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5343907008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-15T00:47:43Z","expires_at":"2026-08-15T14:00:33.959971Z","created_at":"2026-07-15T14:00:32.344861Z","updated_at":"2026-07-16T14:00:34.090494Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/be679834-9c8e-4780-bdad-f2d02b24a22e"},{"id":"d5817836-ec6a-4a44-8482-6cb7a1c60532","company_id":"ab3e4567-6f87-4ccf-9ec0-81fd82105f48","title":"Senior Data Scientist, Detection","slug":"senior-data-scientist-detection-6c1be6df","description":"About Us \n \n At Cloudflare, we are on a mission to help build a better Internet. Today the company runs one of the world’s largest networks that powers millions of websites and other Internet properties for customers ranging from individual bloggers to SMBs to Fortune 500 companies. Cloudflare protects and accelerates any Internet application online without adding hardware, installing software, or changing a line of code. Internet properties powered by Cloudflare all have web traffic routed through its intelligent global network, which gets smarter with every request. As a result, they see significant improvement in performance and a decrease in spam and other attacks. Cloudflare was named to Entrepreneur Magazine’s Top Company Cultures list and ranked among the World’s Most Innovative Companies by Fast Company. \n At Cloudflare, we’re not looking for people who wait for a polished roadmap; we’re looking for the builders who see the cracks in the Internet that everyone else has simply learned to live with. We value candidates who have the instinct to spot a \"normalized\" problem and the AI-native curiosity to create a solution using the latest tools. Our culture is built on iteration, leveraging AI to ship faster today to make it better tomorrow, while ensuring that every improvement, no matter how small, is shared across the team to lift everyone up. If you’re the type of person who values curiosity over bureaucracy, and that AI is a partner in solving tough problems to keep the Internet moving forward, you’ll fit right in.\n Available Locations- New York\n About the Role \n Cloudflare’s Engineering Team is home to some of the industry’s top engineers, dedicated to building and scaling innovative software that handles a huge proportion of the Internet. Our Detection department sits at the heart of that mission: we identify automated, fraudulent, and malicious activity across the Internet and through our gateway. We develop advanced detection systems and machine learning models that operate at scale, collaborating with Product and Engineering teams across the company to protect our customers and stay ahead of the constantly evolving threat landscape.\n Responsibilities \n \n Research, design, and evaluate detection models that identify automated, fraudulent, and malicious activity across Internet-scale data.\n Dig into massive datasets to uncover the patterns and behaviors that distinguish adversaries from legitimate users.\n Define how detection success is measured, designing metrics and evaluation strategies for problems where ground truth is noisy, delayed, or contested.\n Stay current on emerging AI/ML research and evaluate how new techniques (e.g., LLMs, generative AI) can be applied to our products.\n Partner with ML Engineers, Data Engineers, and Product to take detection approaches from research to production and measure their real-world impact.\n \n Desirable Skills, Knowledge, and Experience  \n \n Fraud and bots at scale. You have experience across fraud, abuse, and/or bot detection on large, high-velocity traffic. You may focus on one, but you transfer instincts between them.\n Strong fundamentals, fluent in data. You have solid applied statistics, machine learning, and AI methodology fundamentals. You choose the right technique for the problem, and are fluent with large-scale data.\n You have at least 5-7 years of experience professionally working in Data Science, ML Engineering, or Software Engineering. \n You are very comfortable with Python \u0026 SQL in production environments.\n \n Bonus points \n \n At home in ground truth ambiguity. Building detections when ground truth is scarce is the heart of this job. You make real progress with weak, delayed, or absent labels and you're energized by adversaries that fight back.\n You don't burn signals. You understand (or are curious to learn) how to act on detections without tipping your hand, knowing that how you deploy and respond can erode your future visibility.\n Pragmatic about complexity. You know when a simple solution beats a complex one, and you don't chase small gains at disproportionate cost.\n Disciplined in code. You apply strong programming and engineering best practices in both research and production code.\n Impact-driven and clear. You connect your work to business impact and communicate clearly across technical and non-technical stakeholders.\n \n Compensation \n Compensation may be adjusted depending on work location.\n \n  For New York City based hires: Estimated annual salary of $185,000 - $231,000.\n \n Equity \n This role is eligible to participate in Cloudflare’s equity plan.\n Benefits \n Cloudflare offers a complete package of benefits and programs to support you and your family.  Our benefits programs can help you pay health care expenses, support caregiving, build capital for the future and make life a little easier and fun!  The below is a description of our benefits for employees in the United States, and benefits may vary for employ","salary_min":185000,"salary_max":231000,"location":"Hybrid","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["generative-ai","llm","cloud","data-science"],"apply_url":"https://boards.greenhouse.io/cloudflare/jobs/8042547?gh_jid=8042547","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-15T00:45:56Z","expires_at":"2026-08-15T14:09:57.996669Z","created_at":"2026-07-15T14:11:16.38206Z","updated_at":"2026-07-16T14:09:58.110127Z","company_name":"Cloudflare","company_slug":"cloudflare","company_logo_url":"https://www.google.com/s2/favicons?domain=cloudflare.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/d5817836-ec6a-4a44-8482-6cb7a1c60532"},{"id":"9cf703e4-28cb-47a7-9151-d26f9745f43d","company_id":"74257563-5513-4a8d-a0f7-01f00c59aed6","title":"Senior Machine Learning Engineer, Relevance and Personalization (Query Intelligence)","slug":"senior-machine-learning-engineer-relevance-and-personalization-query-intelligence-b6fdeb9a","description":"Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. \n The Community You Will Join: \n The Relevance and Personalization team at Airbnb is responsible for search and recommendation across the entire Airbnb digital platform. In this role you'll focus on query intelligence, the front door of search working on critical, impactful projects that turn what a guest types, taps, or says into a precise understanding of their intent, spanning autocomplete and smart compose, query tagging, query expansion, and intent modeling across Stays, Experiences, and Services.\n The Difference You Will Make: \n Query understanding is where every search begins, and it directly shapes retrieval, ranking, and ultimately the perfect match between guests and hosts. We build cutting-edge AI technologies across the end-to-end search ranking product stack w.r.t. data pipelines, feature and model innovations, serving and experimentation efficiency, leveraging rich signals from various types of data (structured, sequential, image, text, etc) and increasingly large language models at Airbnb. You'll build the models that parse free-form and natural-language multimodal queries, extract entities and location context, classify intent, and anticipate what guests want before they finish typing. We collaborate closely with teams across Airbnb to develop the ranking solutions and support a healthy marketplace for hosts and guests to further Airbnb's mission of creating a world where people can Belong Anywhere. Some past publications from the team can be found here: https://sites.google.com/view/airbnb-relevance-publications/home \n A Typical Day:  \n \n Work with large scale structured and unstructured data, build and continuously improve cutting edge Machine Learning models for Airbnb product, business and operational use cases, with a focus on query understanding.\n Develop query understanding capabilities — autocomplete and smart compose, query tagging (sequence tagging / NER), query expansion, and query/user intent modeling — and natural-language (\"search in your own words\") search experiences powered by modern NLP and LLMs.\n Work collaboratively with cross-functional partners including software engineers, product managers, operations and data scientists, identify opportunities for business impact, understand, refine, and prioritize requirements for machine learning models, drive engineering decisions, and quantify impact.\n Hands-on develop, productionize, and operate Machine Learning models and pipelines at scale, including both batch and real-time use cases.\n Leverage third-party and in-house Machine Learning tools \u0026 infrastructure to develop reusable, highly differentiating and high-performing Machine Learning systems, enable fast model development, low-latency serving and ease of model quality upkeep.\n Example projects include: smart compose and language generation for search, LLM-based sequence taggers, LLM-driven query/location expansion, intent classification, and user-intent sequence modeling.\n \n Your Expertise: \n \n 5+ years of industry experience in applied Machine Learning, inclusive MS or PhD in relevant fields.\n Strong programming (Scala / Python / Java / C++ or equivalent) and data engineering skills.\n Deep understanding of Machine Learning best practices (eg. training/serving skew minimization, A/B test, feature engineering, feature/model selection), algorithms (eg. neural networks/deep learning, optimization) and domains (eg. natural language processing, personalization, search and recommendation, marketplace optimization).\n Experience with 3 or more of these technologies: Tensorflow, PyTorch, Kubernetes, Spark, Airflow (or equivalent), Kafka (or equivalent), data warehouse (eg. Hive).\n Industry experience building end-to-end Machine Learning models.\n Experience applying large language models and modern NLP — e.g., sequence tagging/NER, text generation, intent classification, or embedding/representation learning.\n Familiarity with building natural-language, AI-native and agentic search experiences is a plus.\n Exposure to architectural patterns of large, high-scale software applications (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, models).\n \n Your Location: \n This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager. While the position is Remote Eligible, you must live in a state where Airbnb, Inc. has a registered entity. Click here for the up-to-date list of excluded states. This list is continuously evolving, so please check back with us if the state you live in is on the exclusion list. If your po","salary_min":200000,"salary_max":235000,"location":"United States","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["tensorflow","agents","search","data-pipeline","deep-learning","generative-ai","llm","pytorch"],"apply_url":"https://careers.airbnb.com/positions/8065789?gh_jid=8065789","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T23:54:51Z","expires_at":"2026-08-15T14:10:04.208902Z","created_at":"2026-07-15T14:11:23.002744Z","updated_at":"2026-07-16T14:10:04.325662Z","company_name":"Airbnb","company_slug":"airbnb","company_logo_url":"https://www.google.com/s2/favicons?domain=airbnb.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9cf703e4-28cb-47a7-9151-d26f9745f43d"},{"id":"021f3b70-f0d5-4666-a5e1-431d120b0e63","company_id":"31ae48bc-c938-4c26-a348-0bf3c089a446","title":"Senior Software Engineer - GPU Kernel Authoring \u0026 Optimization","slug":"senior-software-engineer-gpu-kernel-authoring-optimization-d4eed12b","description":"CoreWeave is The Essential Cloud for AI™. Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025. Learn more at  www.coreweave.com . \n About the role: \n CoreWeave is the top-rated AI-cloud for high-performance GPU infrastructure across AI/ML, visual effects, rendering, and real-time inference. Our stack is engineered for speed, scale, and cost-efficiency—an unmatched alternative to traditional hyperscalers. At CoreWeave, infrastructure is the product.\n We're looking for a Senior Engineer for CoreWeave's Benchmarking \u0026 Performance team, focused on kernel authoring and optimization. You will write, profile, and tune the GPU kernels that sit on the critical path of large-scale model serving—squeezing maximum throughput and minimum latency out of every SM, tensor core, and byte of memory bandwidth. You will also aid us in achieving industry-leading end-to-end performance benchmarking publications such as MLPerf.\n You will be an owner who leads designs, raises engineering standards, and delivers measurable improvements to latency, throughput, and reliability across our inference stack. You'll partner with product, orchestration, and hardware teams to turn kernel-level wins into end-to-end gains and meet strict P99 SLAs at scale.\n \n Author, profile, and optimize CUDA kernels—GEMMs, attention, MoE routing, quantization, KV-cache, and fused epilogues—on the critical path of LLM inference.\n Optimize for the hardware: exploit tensor cores and tune occupancy, memory coalescing, shared-memory/register usage, and overlap of compute with data movement.\n Use kernel-authoring DSLs and compilers to prototype and ship kernels quickly without sacrificing performance.\n Benchmark rigorously: build reproducible microbenchmarks and roofline analyses, and validate that kernel-level wins translate to end-to-end latency/throughput gains across model-serving stacks (vLLM, TensorRT-LLM, llm-d, SGLang).\n Implement and maintain benchmarking workflows for end-to-end MLPerf Inference (and Training) runs, including workload setup, cluster configuration, runbooks, and result validation.\n Lead design reviews and drive architecture within the team; decompose multi-service work into clear milestones.\n Mentor junior engineers; review cross-team designs and elevate coding/testing standards.\n Help ensure reproducible, well-documented benchmarking and kernel-optimization processes.\n \n Who You Are: \n \n 5+ years of experience building high-performance computing, GPU/accelerator software, or performance-critical systems.\n Hands-on CUDA experience is required—you have written and optimized custom kernels and are fluent with the CUDA programming and memory model.\n Deep understanding of GPU architecture and performance: tensor cores, warp/occupancy tuning, the memory hierarchy and bandwidth, NVLink/PCIe, and profiling with Nsight Compute/Systems.\n Strong coding in C++ and Python; comfortable reading and writing low-level, performance-sensitive code.\n Familiarity with model-serving stacks (vLLM, TensorRT-LLM, llm-d, SGLang) and the kernels that dominate their inference cost.\n Strong communicator comfortable collaborating with cross-functional teams and external partners.\n \n Preferred: \n \n Triton or Mojo for authoring custom GPU kernels — highly desired.\n CuTe DSL for Python-based kernel authoring on NVIDIA GPUs.\n JAX and its Pallas kernel language for authoring kernels on GPU/TPU.\n HIP / ROCm and AMD GPU experience.\n NCCL and collective-communication performance.\n Experience with alternative accelerators such as Google TPUs and Meta's MTIA.\n Familiarity with kernel-authoring DSLs and nano-compilers such as KNYFE and its Block DSL.\n Experience with Kubernetes at production scale.\n Experience with SUNK (Slurm on Kubernetes) / Slurm for scheduling large GPU jobs.\n Experience running MLPerf submissions or similar large-scale audited benchmarks.\n Contributions to OSS projects such as vLLM, SGLang, PyTorch, Triton, or CUTLASS.\n \n Wondering if you're a good fit? \n We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren't a 100% skill or experience match.\n Why CoreWeave? \n Help shape an industry-defining inference platform that enables teams to deploy generative AI and real-time applications at scale. If squeezing every last microsecond out of GPU kernels and delivering reliable model serving excites you, this is the place to build. We're in an exciting stage of hyper-growth that you will not want to miss out on. We're not afraid of a little chaos, and we're constantly ","salary_min":182000,"salary_max":242000,"location":"Sunnyvale, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["mlops","pytorch","gpu","generative-ai","llm","jax","computer-graphics"],"apply_url":"https://coreweave.com/careers/job?4697100006\u0026board=coreweave\u0026gh_jid=4697100006","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T22:01:55Z","expires_at":"2026-08-15T14:05:36.795Z","created_at":"2026-07-15T14:06:51.909822Z","updated_at":"2026-07-16T14:05:36.92287Z","company_name":"CoreWeave","company_slug":"coreweave","company_logo_url":"https://www.google.com/s2/favicons?domain=coreweave.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/021f3b70-f0d5-4666-a5e1-431d120b0e63"},{"id":"66be6f1d-738c-4b9b-b07d-4cae69e7b29d","company_id":"a0000000-0000-0000-0000-000000000003","title":"Senior Machine Learning Engineer, Agent Oversight","slug":"senior-machine-learning-engineer-agent-oversight-774633fc","description":"About Scale\n Scale’s mission is to develop reliable AI systems for the world’s most important decisions. As the leading AI data foundry, we provide the high-quality data and full-stack technologies that power the world’s most advanced models — fueling breakthroughs in generative AI, defense, and autonomous vehicles. We partner with leading enterprises and governments to bring AI into production that performs when it matters most, combining rigorous evaluation with full-stack deployment so our customers can build AI they can trust.\n About the Team\n Applied Intelligence Systems team is part of the Scale Generative AI Platform (SGP), focused on pushing the frontier of what agentic applications can do across diverse enterprise and government use cases. We build the infrastructure and tooling that power agentic AI in production, paired with applied ML research, design, and evaluation to ensure these systems perform reliably at the scale our customers demand. We’re growing fast, with increasing traction across both commercial and public sector customers, and we’re just getting started — this team will define what dependable, production-grade agentic AI looks like.\n About the Role\n As a Machine Learning Engineer on Agent Oversight, you will drive the end-to-end lifecycle that ensures our production agents perform reliably and improve over time. This includes building observability tools, designing robust evaluation frameworks, and developing improvement loops. Whether scaling infrastructure or researching new improvement methods, you will navigate the entire ML loop while maintaining rigorous technical standards.\n You will:\n \n Build or contribute to observability into agent behavior in production — the signals and instrumentation needed to actually see what an agent is doing, not just whether it succeeded or failed\n Design evaluation methodologies and metrics for agentic applications, and work with the platform to make them run automatically, at scale, across different customer use cases, not just as one-off analyses\n Build, ship, and own ML systems that detect drift, anomalies, or misalignment in production agent behavior — from first prototype through running reliably at scale\n Design and run rigorous experiments to validate model and agent performance improvements before they ship\n Work alongside software engineers on the platform where your work intersects with broader infrastructure — but you’re expected to take your own work from idea to production, not hand it off\n Collaborate closely with product managers, customers, data annotators, Forward Deployed Engineers, and other engineering teams to translate enterprise and government requirements into robust platform capabilities\n Depending on focus, contribute to novel methods and approaches that push the state of the art for agent evaluation and improvement, or focus on building ML systems that hold up reliably at scale in production\n \n Requirements:\n \n 5+ years of experience as an ML engineer or applied scientist, ideally on a production ML or LLM-powered system — not just consuming a third-party ML API within a feature\n Strong grounding in  at least two  of the following:\n \n Building or scaling evaluation, monitoring, or continuous-learning infrastructure for ML/agentic systems\n Design experience for agent systems (architecture, orchestration, tool use)\n Developing new methods, reward models, or model training/fine-tuning approaches\n \n Hands-on experience with LLMs and agent architectures — tool use, planning, multi-agent orchestration\n Comfortable partnering with software engineers to productionize research and experimental work, not just deliver a one-off analysis\n Rigorous approach to experimentation: clear hypotheses, real statistical grounding, and results that hold up under scrutiny\n Track record of collaborating across functions (Product, Forward Deployed Engineering, etc.) to navigate ambiguous requirements and bring them to production\n Gives direct, substantive feedback on designs and code, and takes it the same way — and mentors others as they grow\n \n Nice to have:\n \n Experience building or contributing to RLHF, SFT, or other fine-tuning/RL workflows, reward modeling, or verifiable-reward systems\n Experience with model or systems optimization (e.g., latency, cost, or inference efficiency)\n Published research, open-source contributions, or patents in agentic systems, LLMs, or applied ML\n Experience working in regulated or enterprise contexts\n Track record of taking a novel method from prototype to something running reliably in production, navigating ambiguity along the way\n Experience reviewing others’ technical designs or mentoring engineers at a senior/staff level\n Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career level","salary_min":216000,"salary_max":270000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","reinforcement-learning","agents","generative-ai","autonomous-vehicles","fine-tuning","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/scaleai/jobs/4714527005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T20:14:32Z","expires_at":"2026-08-15T14:01:43.524986Z","created_at":"2026-07-15T14:01:47.280877Z","updated_at":"2026-07-16T14:01:43.716585Z","company_name":"Scale AI","company_slug":"scale-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=scale.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/66be6f1d-738c-4b9b-b07d-4cae69e7b29d"},{"id":"96c4b57f-c214-4de0-829c-cda4957c7a17","company_id":"a0000000-0000-0000-0000-000000000003","title":"Senior Software Engineer, Agent Oversight","slug":"senior-software-engineer-agent-oversight-a8682235","description":"About Scale\n Scale’s mission is to develop reliable AI systems for the world’s most important decisions. As the leading AI data foundry, we provide the high-quality data and full-stack technologies that power the world’s most advanced models — fueling breakthroughs in generative AI, defense, and autonomous vehicles. We partner with leading enterprises and governments to bring AI into production that performs when it matters most, combining rigorous evaluation with full-stack deployment so our customers can build AI they can trust.\n About the Team\n Applied Intelligence Systems team is part of the Scale Generative AI Platform (SGP), focused on pushing the frontier of what agentic applications can do across diverse enterprise and government use cases. We build the infrastructure and tooling that power Agentic AI in production, paired with applied ML research, design, and evaluation to ensure these systems perform reliably at the scale our customers demand. We’re growing fast, with increasing traction across both commercial and public sector customers, and we’re just getting started — this team will define what dependable, production-grade agentic AI looks like.\n About the Role\n As a Software Engineer on Agent Oversight, you will build the platform infrastructure that lets our production agents be observed, evaluated, and improved at scale. This includes building observability tooling, evaluation harnesses, and the pipelines that connect them to improvement loops. Whether building foundational infrastructure or partnering closely with ML engineers on production workflows, you will own your systems end-to-end while maintaining rigorous technical standards.\n You will:\n \n Design and build core platform capabilities for deploying, monitoring, and evaluating agentic applications in production\n Build reliable APIs and data pipelines that capture agent telemetry, evaluation signals, and performance metrics at scale\n Work alongside ML engineers where platform work intersects with evaluation or improvement systems — bringing enough ML fluency to reason about model behavior, evaluation quality, and improvement loops while owning the software systems that make those workflows reliable\n Own the reliability, scalability, and observability of platform components serving multiple concurrent enterprise and government customers\n Work cross-functionally with product, forward deployed engineering, and customers to translate real-world deployment requirements into platform features\n Build features end-to-end: system design, implementation, debugging, and testing\n Participate in high-velocity experimentation to validate platform capabilities against real customer usage\n \n Requirements:\n \n 4+ years of professional software engineering experience, with strong fundamentals in backend/distributed systems, APIs, and data pipeline design\n Hands-on experience building production software for ML/LLM-powered products or platforms, such as evaluation systems, observability/monitoring, experimentation infrastructure, agent runtimes, model-serving-adjacent services, or telemetry/data pipelines\n Working knowledge of how LLM or ML systems behave in production: evaluation signals, failure modes, prompt/tool-calling workflows, experiment results, data quality issues, and the tradeoffs between offline evals and live customer behavior\n Experience partnering closely with ML engineers or applied researchers to turn prototypes, eval loops, or model-improvement workflows into reliable platform capabilities, without needing to own model training, modeling strategy, or research direction\n Experience building infrastructure or platforms that other engineering teams build on top of (internal platform, developer tools, or similar)\n Track record of taking ownership of features or components end-to-end — from design through production — within a larger platform or system\n Comfortable operating in an ambiguous, fast-changing domain where tooling and best practices are still being defined\n Strong problem-solving skills and the ability to work independently or as part of a tight-knit, cross-functional team\n Excited to work directly with ML engineers and customer-facing teams, including challenging assumptions in designs and metrics when platform behavior, model behavior, and customer needs intersect\n Gives direct, substantive feedback on designs and code, and takes it the same way — and mentors others as they grow\n \n Nice to have:\n \n Deep experience building or maintaining observability, monitoring, or evaluation systems for ML/LLM-powered products in production\n Familiarity with agent architectures — tool use, planning, multi-agent orchestration\n Exposure to MLOps, feature stores, model serving, or experiment infrastructure\n Experience working in regulated or enterprise contexts\n Experience reviewing others’ technical designs or mentoring engineers at a senior/staff level\n Compensation packages at Scale for eligible roles include base s","salary_min":216000,"salary_max":270000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["mlops","autonomous-vehicles","llm","data-pipeline","distributed-systems","agents","generative-ai"],"apply_url":"https://job-boards.greenhouse.io/scaleai/jobs/4714509005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T20:12:46Z","expires_at":"2026-08-15T14:01:43.763646Z","created_at":"2026-07-15T14:01:47.543291Z","updated_at":"2026-07-16T14:01:43.880972Z","company_name":"Scale AI","company_slug":"scale-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=scale.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/96c4b57f-c214-4de0-829c-cda4957c7a17"},{"id":"45810c38-51f8-47ab-83a8-b91cb2515162","company_id":"a0000000-0000-0000-0000-000000000001","title":"Safeguards Enforcement Analyst, Fraud \u0026 Scams","slug":"safeguards-enforcement-analyst-fraud-scams-028dd4c2","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the role\n As a Safeguards Enforcement Analyst on the account abuse team, you'll build and execute enforcement workflows that keep our products safe, with a focus on detecting and mitigating potential harm. Your initial focus will be standing up fraud \u0026 scams enforcement as a program: today this work is handled reactively and in fragments — payment fraud, promotional abuse, and scam-pattern enforcement don't yet have a single owner. You'll be that owner: defining the policy area, building the detection-to-enforcement pipeline, and setting the operating model that a contractor bench can execute against. The surface area is broad: payment fraud (stolen cards, chargebacks, disputes), promotional and credits abuse, and the use of accounts to run scams against third parties.\n This position may expand into broader areas of enforcement over time. Safety is core to our mission, and you'll help shape policy enforcement so that our users can safely interact with and build on top of our products in a harmless, helpful, and honest way.\n Key responsibilities \n \n Define the fraud \u0026 scams policy taxonomy and how cases are classified, prioritized, and escalated\n Investigate and dismantle organized abuse rings, converting findings into durable controls\n Stand up proactive fraud detection and customer-facing communication flows for fraudulent organization cases\n Build the dispute and chargeback strategy in partnership with payments and card-network partners\n Quantify fraud losses and control efficacy to drive investment decisions\n Author the contractor playbook for fraud review and own QA of scaled output\n Keep up to date with emerging AI policy enforcement best practices, and use these to inform our decision-making and workflows\n \n Minimum qualifications\n \n Deep payment-fraud experience at a fintech, marketplace, or platform — chargebacks, disputes, card-testing, promotional abuse\n Experience building or significantly scaling a fraud program from an early state, not just operating a mature one\n A working command of payment-network and dispute mechanics, sufficient to make strategy calls on them\n Comfort being the sole owner of an area: prioritizing ruthlessly, shipping iteratively, and asking for help precisely\n Comfort using data (SQL or similar tools) to quantify fraud losses and control efficacy\n Strong written communication skills, with experience producing clear briefs and recommendations for technical and non-technical stakeholders\n Excellent judgment and the ability to collaborate with team members while navigating rapidly evolving priorities and workstreams\n \n Preferred qualifications\n \n Experience working directly with payment service providers and card networks on fraud strategy\n Experience with scam typologies beyond payments — social engineering, impersonation, platform-mediated scams\n Experience managing vendor relationships in the fraud/risk detection space\n A deep interest in AI safety and responsible technology development\n Experience writing effective prompts for generative AI systems in a content review or enforcement context\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $245,000 — $285,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\n We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application ","salary_min":245000,"salary_max":285000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["alignment","payments","generative-ai","rust"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5319554008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T03:10:05Z","expires_at":"2026-08-15T14:00:33.490419Z","created_at":"2026-07-15T14:00:31.991198Z","updated_at":"2026-07-16T14:00:33.634Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/45810c38-51f8-47ab-83a8-b91cb2515162"},{"id":"6fc9c11a-5817-4f35-acb7-d96f007c4325","company_id":"a0000000-0000-0000-0000-000000000001","title":"Safeguards Enforcement Analyst, Ban Evasion \u0026 Recidivism","slug":"safeguards-enforcement-analyst-ban-evasion-recidivism-2b8cbefe","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the role \n As a Safeguards Enforcement Analyst on the account abuse team, you'll build and execute enforcement workflows that keep our products safe, with a focus on detecting and mitigating potential harm. Your initial focus will be recidivism: a ban that an actor can evade in five minutes isn't enforcement — it's friction. You'll own detecting when banned actors return, linking accounts across identities, and closing the re-registration paths that matter most. The mandate includes our highest-stakes populations, including preventing evasion of child-safety enforcement bans, where the cost of a missed return is unacceptable.\n This position may expand into broader areas of enforcement over time. Safety is core to our mission, and you'll help shape policy enforcement so that our users can safely interact with and build on top of our products in a harmless, helpful, and honest way.\n Key responsibilities \n \n Investigate evasion clusters end to end — from a single appeal or signal anomaly to the full linked actor network\n Convert individual findings into durable systemic controls and detection proposals\n Operationalize re-registration controls for high-severity ban populations\n Partner with Engineering and Data Science teams on account-linking signals to connect returning actors across identities\n Build the recidivism measurement framework: how often banned actors return, how fast we catch them, and which controls reduce return rates\n Author playbooks for contractor-supported evasion review with QA against your own gold standard\n Keep up to date with emerging AI policy enforcement best practices, and use these to inform our decision-making and workflows\n \n Minimum qualifications\n \n Experience investigating ban evasion, multi-accounting, or repeat fraud actors at a platform with adversarial users\n Fluency in SQL and comfort building your own analyses across large account and event datasets\n Experience working with fraud or identity-linking signals and a working understanding of their precision/recall tradeoffs\n Rigor about evidence standards — comfort with the asymmetric cost of false positives in severe-harm enforcement\n A track record of turning one-off investigations into repeatable detection logic and policy\n Strong written communication skills, with experience producing clear briefs and recommendations for technical and non-technical stakeholders\n Excellent judgment and the ability to collaborate with team members while navigating rapidly evolving priorities and workstreams\n \n Preferred qualifications\n \n Experience using payment or network risk signals in an enforcement context\n Experience with child-safety or other high-severity integrity enforcement\n Experience collaborating directly with detection engineering or data science teams on rule deployment\n A deep interest in AI safety and responsible technology development\n Experience writing effective prompts for generative AI systems in a content review or enforcement context\n \n  \n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $245,000 — $285,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\n We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous","salary_min":245000,"salary_max":285000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["payments","alignment","generative-ai","rust"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5319592008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T03:07:48Z","expires_at":"2026-08-15T14:00:32.807813Z","created_at":"2026-07-15T14:00:31.46483Z","updated_at":"2026-07-16T14:00:32.989509Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/6fc9c11a-5817-4f35-acb7-d96f007c4325"},{"id":"b5fee987-f2ea-4b80-a04f-395e616158d8","company_id":"c93e0284-9c76-4a85-9905-494865ab9278","title":"AI Systems Performance Engineer - New Graduate","slug":"ai-systems-performance-engineer-new-graduate-e4bfa2f7","description":"The era of pervasive AI has arrived. In this era, organizations will use generative AI to unlock hidden value in their data, accelerate processes, reduce costs, drive efficiency and innovation to fundamentally transform their businesses and operations at scale. \n SambaNova Suite™ is the first full-stack, generative AI platform, from chip to model, optimized for enterprise and government organizations. Powered by the intelligent SN40L chip, the SambaNova Suite is a fully integrated platform, delivered on-premises or in the cloud, combined with state-of-the-art open-source models that can be easily and securely fine-tuned using customer data for greater accuracy. Once adapted with customer data, customers retain model ownership in perpetuity, so they can turn generative AI into one of their most valuable assets. \n About The Role \n We are seeking a talented and highly motivated AI Systems Performance Engineer to bring up and optimize state-of-the-art foundation models on SambaNova's reconfigurable dataflow platform.\n You'll work hands-on with advanced AI models — such as DeepSeek, GLM, Kimi, GPT OSS, Llama, Qwen, and other frontier architectures — and learn how modern AI systems achieve high throughput, low latency, and efficient large-scale inference.\n In this role, you'll work at the intersection of machine learning and computer systems, collaborating with engineers across model, compiler, runtime, and hardware teams. This is an ideal opportunity for a new graduate who is passionate about understanding how AI models execute on real hardware and wants to help build the next generation of high-performance AI systems.\n Responsibilities \n \n Bring up cutting-edge foundation models, including LLMs and multimodal models, on the SambaNova platform through the SambaNova software stack.\n Analyze and profile model execution to identify performance bottlenecks across model, compiler, runtime, and hardware layers.\n Optimize AI workloads for throughput, latency, memory efficiency, and scalability.\n Collaborate with machine learning, compiler, runtime, and hardware engineers to develop high-performance AI applications.\n Explore and integrate new techniques in model architecture, quantization, scheduling, caching, and memory optimization.\n Develop tools, benchmarks, and performance analysis methodologies for large-scale AI inference.\n Investigate new model architectures and translate research advances into efficient implementations on production AI systems.\n Contribute ideas for dataflow, scheduling, and system optimizations for both single-node and distributed inference.\n \n Basic Qualifications \n \n Bachelor's or Master's degree in computer science, electrical engineering, computer engineering, or a related technical field (e.g., applied mathematics, physics, or statistics), completed or expected before the start date.\n Strong programming skills in Python, C++, or a similar programming language.\n Solid foundations in algorithms, data structures, computer architecture, operating systems, or parallel computing.\n Familiarity with deep learning and at least one major ML framework, such as PyTorch, TensorFlow, or JAX.\n Strong analytical and problem-solving skills, with an interest in understanding and optimizing system performance.\n Ability and enthusiasm to learn across machine learning, software systems, and hardware.\n \n Preferred Qualifications \n \n Coursework, research, internship, or project experience in machine learning systems, computer architecture, compilers, distributed systems, or high-performance computing.\n Hands-on experience with LLMs, multimodal models, or transformer architectures.\n Familiarity with model inference, KV cache, batching, quantization, or distributed execution.\n Experience with GPU or accelerator programming using CUDA, Triton, OpenCL, or similar technologies.\n Familiarity with frameworks such as vLLM, DeepSpeed, Megatron, or TensorRT.\n Understanding of memory hierarchy, caching, parallelism, or scheduling.\n Experience profiling and optimizing the performance of software or ML workloads.\n Research publications, open-source contributions, programming competitions, or technically challenging personal projects are a plus.\n \n We value strong technical fundamentals, curiosity, and the ability to learn quickly. Prior production experience with large-scale AI systems is not required.\n Base Salary Range:\n Base Pay Range\n $135,000 — $165,000 USD \n Submission Guidelines Please note that in order to be considered an applicant for any position at SambaNova Systems, you must submit an application form for each position for which you believe you are qualified.  \n EEO Policy SambaNova Systems is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, military or veteran status, national origin/ancestry, race, religion, creed, sex ","salary_min":135000,"salary_max":165000,"location":"San Jose, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["pytorch","tensorflow","generative-ai","distributed-systems","gpu","llm","deep-learning"],"apply_url":"https://sambanova.ai/sambanova-available-positions/?gh_jid=6115124004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-13T22:28:28Z","expires_at":"2026-08-15T14:04:51.08642Z","created_at":"2026-07-15T14:06:10.360035Z","updated_at":"2026-07-16T14:04:51.213909Z","company_name":"SambaNova Systems","company_slug":"sambanova","company_logo_url":"https://www.google.com/s2/favicons?domain=sambanova.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/b5fee987-f2ea-4b80-a04f-395e616158d8"},{"id":"e57cf48d-3756-4016-8e50-400a76bbaa5d","company_id":"714f360f-a244-487d-b3f0-0c43518a9e66","title":"Staff Machine Learning Engineer, Computer Vision","slug":"staff-machine-learning-engineer-computer-vision-147d8a7f","description":"About Pinterest: \n Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.\n Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the  flexibility to do your best work. Creating a career you love? It’s Possible.\n At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.\n Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here .\n Within Pinterest, the Pinterest Labs organization focuses on applied ML research and development. Labs works across a broad variety of AI/ML initiatives—including core computer vision, multimodal representation learning, heterogeneous graph neural networks, generative modeling, and recommender systems. This is the group that develops the foundation ML models that fully leverage the tens of billions of Pins and the associated knowledge graph to improve the core product.\n We are currently hiring for the Visual Modeling team in Labs, which develops Pinterest's in-house visual encoder. In this role, you'll work with Pinterest's rich visual-text dataset to train large-scale models from scratch that are continuously shipped to production to power visualization features. You'll build multimodal representations that power applications such as recommender systems, Semantic IDs, and a range of downstream ML models. The visual encoder also produces visual tokens that power our in-house VLM and composed image retrieval models. The core visual pod is a small group (~10 engineers) inside Labs, which allows for deep collaboration. For example, engineers working on multimodal representation also contribute to our internal text-to-image generation Canvas project—collaborating on autoencoder design or on reward function development for RL training.\n  \n What you’ll do: \n \n Prototype state-of-the-art visual encoders that power Pinterest's recommender systems and internal visual language models.\n Experiment with billion-scale datasets and gain hands-on experience with large-scale GPU computing.\n Build flexible visual reasoning tools such as composed image retrieval, promptable detection/segmentation, and instruction-tuned embedding and generative models.\n Read research papers, participate in group discussions, and help brainstorm the company's overall visual generative strategy.\n Help collect relevant visual instruction training data that can be shared across multimodal representation, composed image retrieval, text-to-image generation and visual language modeling.\n Publish and share your work through conferences, paper submissions, and blog posts.\n Mentor junior researchers and research interns within the Pinterest Labs organization.\n  \n \n What we’re looking for: \n \n Research engineers and scientists with experience building and training computer vision models.\n Experience with multimodal representations and visual language modeling is strongly preferred.\n A track record of research contributions (e.g., publications, open-source work) and/or shipping ML models to production.\n Hands-on experience with large-scale model training and modern deep learning frameworks (e.g., PyTorch).\n Strong collaboration skills and a demonstrated ability to work effectively in a small, fast-moving team.\n M.S. or PhD in Machine Learning or related academic areas, or equivalent work experience.\n Publications at top ML conferences\n Experience using Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring\n \n  \n Relocation Statement: \n \n This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.\n \n  \n In-Office Requirement Statement: \n \n We let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key moments of collaboration and connection.\n This role will need to be in the office for in-person collaboration 1-2 times/quarter and therefore can be situated anywhere in the country.\n \n  \n #LI-REMOTE #LI-AK7\n At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The posit","salary_min":189308,"salary_max":389753,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"lead","tags":["search","deep-learning","generative-ai","code-generation","pytorch","computer-vision","machine-learning"],"apply_url":"https://www.pinterestcareers.com/jobs/?gh_jid=8015537","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-13T17:51:37Z","expires_at":"2026-08-15T14:09:24.364141Z","created_at":"2026-07-15T14:10:33.975738Z","updated_at":"2026-07-16T14:09:24.488319Z","company_name":"Pinterest","company_slug":"pinterest","company_logo_url":"https://www.google.com/s2/favicons?domain=www.pinterest.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e57cf48d-3756-4016-8e50-400a76bbaa5d"},{"id":"c7233a70-8734-418e-977e-e64fadd481c4","company_id":"a0000000-0000-0000-0000-000000000001","title":"Safeguards Enforcement Analyst, Cyber Harm ","slug":"safeguards-enforcement-analyst-cyber-harm-3292bc78","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the role\n As an Enforcement Analyst, you will be responsible for reviewing content and executing enforcement actions across our products and services, with a focus on detecting and mitigating attempts to misuse Anthropic's AI systems for malicious cyber operations. Your initial focus will center on reviewing flagged activity related to cyberattacks, malware development, and offensive exploitation; however, this position may later expand to include broader areas of enforcement.\n Safety is core to our mission, and you'll help uphold policy enforcement so that our users can safely interact with and build on top of our products in a harmless, helpful, and honest way.\n Important context for this role: In this position you may be exposed to and engage with explicit content spanning a range of topics, including those of a violent, technical, or psychologically disturbing nature. This role may require responding to escalations during weekends and holidays.\n Key responsibilities\n \n Review flagged content and accounts to make accurate, well-documented enforcement decisions in line with our usage policies\n Detect and mitigate potential misuse of AI systems to facilitate cyberattacks, malware creation, exploitation tooling, and related harmful cyber operations\n Triage and escalate novel, ambiguous, or high-severity cases to appropriate stakeholders\n Provide detailed feedback to the Safeguards policy design team on policy gaps surfaced through real enforcement scenarios\n Partner with Engineering and Data Science teams by surfacing detection model errors and quality signals from review to improve precision and recall\n Maintain high accuracy and consistency standards across review queues\n Keep up to date with emerging AI policy enforcement best practices, threat actor tactics, and the evolving cyber threat landscape, using these to inform enforcement decisions\n \n Minimum Qualifications\n \n Experience in cybersecurity, including knowledge of offensive techniques, exploit development, malware analysis, or vulnerability research\n Experience performing content review, abuse investigations, or policy enforcement at volume\n Proficiency in SQL and/or Python for data analysis and threat detection\n Experience identifying emerging risks and communicating findings to a diverse set of stakeholders, such as Product, Policy, Engineering, and Legal teams\n Experience working with generative AI products, including writing effective prompts for content review and enforcement\n \n Preferred qualifications\n \n Experience in trust \u0026 safety, abuse investigations, cybersecurity investigations, or threat intelligence in a technology or AI company\n Experience with large language models and an understanding of how AI technology could be misused for cyber operations\n Experience operating within abuse monitoring programs or enforcement review systems\n Understanding of the challenges involved in implementing product policies at scale, including in the content moderation space\n Experience working with government agencies, regulated environments, or information sharing communities\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $285,000 — $330,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\n We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application ","salary_min":285000,"salary_max":330000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["alignment","llm","security","generative-ai","rust"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5311159008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-10T23:43:23Z","expires_at":"2026-08-15T14:00:33.395422Z","created_at":"2026-07-12T14:00:28.485977Z","updated_at":"2026-07-16T14:00:33.530449Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/c7233a70-8734-418e-977e-e64fadd481c4"},{"id":"cd912b35-ee12-4a3e-90a8-0abe74a80b0d","company_id":"a0000000-0000-0000-0000-000000000001","title":"Safeguards Enforcement Analyst, Integrity \u0026 Authenticity ","slug":"safeguards-enforcement-analyst-integrity-authenticity-941cc4f1","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the role\n As a Safeguards Analyst focusing on Integrity \u0026 Authenticity, you will be responsible for building and executing enforcement workflows for our products and services, with a focus on detecting and mitigating attempts to misuse Anthropic's AI systems for coordinated inauthentic behavior, election manipulation, and targeting, tracking, and surveillance of individuals.\n Your work will span a broad and interconnected set of harm areas: AI-enabled influence operations and disinformation campaigns, the abuse of AI to interfere with electoral processes, and the use of AI systems to facilitate stalking, surveillance, profiling, and the targeting of individuals or groups. Safety is core to our mission, and you'll help shape policy enforcement so that our users can safely interact with and build on top of our products in a harmless, helpful, and honest way.\n Important context for this role: In this position you may be exposed to and engage with explicit content spanning a range of topics, including those of a political, violent, or psychologically disturbing nature. This role may require responding to escalations during weekends and holidays, particularly around major electoral events. \n Key responsibilities\n \n Design and architect automated enforcement systems and review workflows that scale effectively while maintaining high accuracy\n Partner with Engineering and Data Science teams to optimize detection models for policy violations and automated enforcement systems\n Review flagged content to drive enforcement and policy improvements\n Enforce usage policies with a focus on detecting and mitigating AI-enabled influence operations, coordinated inauthentic behavior, election interference, and targeting, tracking, or surveillance of individuals and groups\n Support the Safeguards policy design team by providing detailed feedback on policy gaps based on real enforcement scenarios\n Keep up to date with emerging AI policy enforcement best practices, evolving threat actor tactics, and the regulatory landscape around elections, privacy, and surveillance, using these to inform our decision-making and workflows\n \n Minimum qualifications\n \n Experience in trust \u0026 safety, policy enforcement, threat intelligence, or a closely related field with a focus on one or more of: influence operations, disinformation, coordinated inauthentic behavior, election integrity, or privacy and surveillance harms\n Experience standing up and scaling policy enforcement or content review workflows\n Proficiency in SQL and/or other data analysis tools to draw insights from large datasets\n Experience identifying emerging risks and threat actors, and communicating findings to a diverse set of stakeholders, such as Product, Policy, Engineering, and Legal teams\n Experience working with generative AI products, including writing effective prompts for content review and enforcement\n Understanding of the challenges involved in implementing product policies at scale, including in the content moderation space\n \n Preferred qualifications\n \n Experience conducting cross-platform investigations into influence operations, coordinated inauthentic behavior, or disinformation campaigns\n Familiarity with open-source intelligence (OSINT) techniques and tools used for threat actor tracking and network analysis\n Working knowledge of privacy law, surveillance technology, or data broker ecosystems as they relate to targeting and tracking harms\n Experience with large language models and an understanding of how AI technology could be misused to generate synthetic personas, fabricate quotes, or automate persuasion at scale\n Familiarity with election security frameworks, campaign finance law, or electoral integrity standards in one or more jurisdictions\n Experience navigating evolving regulatory landscapes relevant to this space (e.g., DSA, EU AI Act, FEC regulations, GDPR)\n Experience working with election bodies, civil society organizations, or government agencies on integrity or disinformation-related issues\n Proficiency in Python for data analysis and automation\n Experience with dark web monitoring or tracking threat actors across surface, deep, and dark web environments\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $285,000 — $330,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A ","salary_min":285000,"salary_max":330000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["alignment","llm","generative-ai","rust"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5311149008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-10T23:40:28Z","expires_at":"2026-08-15T14:00:33.599212Z","created_at":"2026-07-12T14:00:28.575559Z","updated_at":"2026-07-16T14:00:33.723625Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/cd912b35-ee12-4a3e-90a8-0abe74a80b0d"},{"id":"f85cfe22-626a-4ca6-a996-ecbec9f694e8","company_id":"0565e120-4260-434a-91f5-7009f7fcbbab","title":"Staff Software Engineer, AI Foundations (Agent Optimization)","slug":"staff-software-engineer-ai-foundations-agent-optimization-51cb0084","description":"About Us \n Temporal is an open source programming model that can simplify code, make applications more reliable, and help developers focus on the important things like delivering features faster. We are on a mission to be the reliable foundation of every developer’s toolbox, and are building the team that will make that happen.\n  \n Our values guide us —they are present in how we show up, make decisions, and work together to make an impact. We’re curious, driven, collaborative, genuine and humble.\n  \n Temporal is growing and we are looking for those who share our values, challenge 'standard' thinking, and want to influence our future. If you have a passion for improving the developer experience, building world-class open-source software and communities, and want to be a part of our amazing team, we'd love to hear from you!\n \n About Us \n Temporal is an open source programming model that can simplify code, make applications more reliable, and help developers focus on the important things like delivering features faster. We are on a mission to be the reliable foundation of every developer’s toolbox, and are building the team that will make that happen.\n Our values guide us—they are present in how we show up, make decisions, and work together to make an impact. We’re curious, driven, collaborative, genuine and humble.\n Temporal is growing and we are looking for those who share our values, challenge 'standard' thinking, and want to influence our future. If you have a passion for improving the developer experience, building world-class open-source software and communities, and want to be a part of our amazing team, we'd love to hear from you!\n Summary \n We have an opening to hire a Staff Software Engineer - Agent Optimization \n Temporal provides a reliable foundation powering AI leaders such as OpenAI, NVIDIA, Cursor, Lovable, Replit, and others. Its adoption is expanding to users spanning a broad range of AI applications ranging from agents to data pipelines and everything in between.\n The mission of the AI Foundations team is to accelerate Temporal adoption across the entire ecosystem. Our approach combines a deep understanding of use cases with rigorous application of computer systems and software design principles.\n In this role, you will lead our agent agent optimization efforts. You will design tools and mechanisms to help Temporal users build agents that are optimized for token spend and response time while maintaining result quality. Model routing is a first step, but represents the tip of the iceberg of techniques that we can apply. For example, multi-agent architecture, cache policy, and context management are all relevant. Candidates for this role should have direct experience with this problem domain.\n You will work closely with other AI Foundations team members, e.g., those who focus on agentic development, maintaining a set of agents skills that lift performance of Codex, Claude Code, and similar tools for developers of Temporal applications. Other team members build ecosystem integrations or develop policy and security systems.\n If you thrive on blending theory and practice, then this is the right team for you. We are an action-oriented group that loves to ship fast and solve customer problems. We also seek thorough technical grounding for our work and invest in systems and practices that foster long-term success.\n Most of Temporal’s work is open source—see for yourself here: https://github.com/temporalio [new window]\n What You Will Do \n \n Work as a software engineer\n Maintain and expand a deep understanding of agentic coding\n Design and build agentic coding systems that we can trust to deliver high-quality outputs\n Develop a deep understanding of AI application development patterns and techniques, including emerging approaches and architectures.\n Take end-to-end ownership of new features, working with other teams to deliver exceptional reliability and a great developer experience.\n Work with multiple programming languages: Python and TypeScript, Java, Go.\n Serve as a domain expert on AI design patterns, collaborating with field staff to provide best-practices and canonical examples.\n Work directly with our developer community to debug issues that need expert attention and get feedback on Temporal features and APIs.\n Write public technical documentation describing Temporal concepts and APIs.\n Go the extra mile to support a customer in need, on the rare occasion that our teams’ engineering expertise is needed.\n Travel to meet your coworkers for a week once or twice a year.\n Attend the occasional developer conference to talk about how great Temporal is (optional).\n \n What You Won’t Do \n \n Work as a Data Scientist, Data Analyst, Devops SWE, or SRE.\n Work in an office (unless you want to, but you’d be by yourself). Temporal is a fully-remote company.\n Commit code that’s poorly-tested or works “most of the time”. 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Meshy makes it effortless for both professional artists and hobbyists to create unique 3D assets—turning text and images into stunning 3D models in just minutes. What once took weeks and cost $1,000 now takes just 2 minutes and $1.\n\nOur world-class team of top experts in computer graphics, AI, and art includes alumni from MIT, Stanford, and Berkeley, as well as veterans from Nvidia and Microsoft. Our talent spans the globe, with team members distributed across North America, Asia, and Oceania, fostering a diverse and innovative multi-regional culture focused on solving global 3D challenges. Meshy is trusted by top developers, backed by premiere venture capital firms like Sequoia and GGV, and has successfully raised $52 Million in funding.\n\nMeshy is the market leader, recognized as the No.1 in popularity among 3D AI tools (according to 2024 A16Z Games) and No.1 in website traffic (according to SimilarWeb, with 3 Million monthly visits). The platform boasts over 5 Million users and has generated 40 Million models.\n\nFounder and CEO Yuanming (Ethan) Hu earned his Ph.D. in graphics and AI from MIT, where he developed the acclaimed Taichi GPU programming language (27K stars on GitHub, used by 300+ institutes). His work is highly influential, including an honorable mention for the SIGGRAPH 2022 Outstanding Doctoral Dissertation Award and over 2,700 research citations.\n\n\n\n\n\nABOUT THE ROLE\n\n - This role sits at the intersection of platform engineering, site reliability, and applied ML systems. The function owns the reliability, scalability, and operability of Meshy's AI model serving stack, along with core engineering infrastructure. The team operates a conventional production infrastructure (CI/CD, build systems, deployment, runtime environments) and develops a model-serving platform that connects the models developed by our Research Team to product-facing backend systems. The position is systems-heavy, production-oriented, and focused on turning experimental model artifacts into robust, observable, and cost-efficient services.\n\n\n\n\n\nJOB RESPONSIBILITIES\n\n - Responsible for the design, development, and optimization of core capabilities for the AI inference platform, including key modules such as inference services, task scheduling, service orchestration, elastic scaling, and release governance.\n\n - Participate in the development of CPU/GPU resource management systems to optimize stability, resource utilization, and cost efficiency in scenarios where online inference and training tasks are run on the same cluster.\n\n - Drive the unified management and scheduling of GPU resources, and explore the practical implementation of capabilities such as MIG, MPS, time-sharing, and virtualization in real-world business operations.\n\n - Continuously optimize the throughput, latency, and availability of the inference pipeline, refining engineering quality in complex inference pipelines, multi-model collaboration, and high-concurrency scenarios.\n\n - Focus on R\u0026D efficiency, resource and cost management, online stability, and disaster recovery architecture design to drive the company’s continuous evolution in performance, reliability, and maintainability.\n\n - Explore AI-native infrastructure and automated operations to make infrastructure smarter and more user-friendly, supporting the company’s rapid expansion during its startup phase.\n\n \n\n\nQUALIFICATIONS\n\n - Bachelor’s degree or higher; majors in Computer Science, Software Engineering, Artificial Intelligence, Telecommunications, or related fields are preferred.\n\n - 1 to 3 years of experience in backend development, infrastructure, cloud-native platforms, machine learning platforms, or AI platforms.\n\n - Proficiency in at least one of Go or Python, with solid software engineering skills and a strong commitment to code quality.\n\n - Understanding of fundamental principles in Linux, operating systems, computer networks, and distributed systems; ability to independently identify and resolve complex engineering issues.\n\n - Practical development experience with Kubernetes, Docker, microservices, or distributed systems, with a basic understanding of production system stability.\n\n - Real-world project experience in areas such as model inference, task orchestration, resource scheduling, and service stability—beyond mere conceptual understanding.\n\n - Self-motivated, curious, and a fast learner; willing to take on greater ownership and broader responsibilities in a startup environment, while continuously learning and quickly adopting new technologies.\n\n\nNICE TO HAVE\n\n - Experience with GPU inference platforms, Kubernetes schedulers, Device Plugins, or related platform development.\n\n - Familiarity with frameworks such as Ray and Ray Serve, or experience in developing and optimizing model serving, distributed in","salary_min":175000,"salary_max":300000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["mlops","distributed-systems","agents","microservices","generative-ai","infrastructure"],"apply_url":"https://jobs.ashbyhq.com/meshy/e82eca7a-4704-4af3-a84f-94c6fb5e1034/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-10T21:33:17.539Z","expires_at":"2026-08-15T14:10:57.375606Z","created_at":"2026-04-13T15:01:38.817296Z","updated_at":"2026-07-16T14:10:57.489777Z","company_name":"Meshy","company_slug":"meshy","company_logo_url":"https://www.google.com/s2/favicons?domain=meshy.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/da65e8fc-123b-47b4-a19f-f1b5fde0fc84"}],"market_demand_pack":{"amount_cents":2900,"api_checkout_url":"https://aidevboard.com/api/v1/checkout?product_id=aidevboard_ai_skills_demand_pack","checkout_url":"https://aidevboard.com/market-demand-pack?qc=api-jobs-market-demand-pack\u0026utm_campaign=skills_demand_pack\u0026utm_medium=jobs_api\u0026utm_source=api","currency":"USD","description":"Full ranked public AI/ML demand CSV, source job URLs, and decision brief with market and offer angles.","fulfillment":"automatic_email_after_paid_checkout","human_checkout_url":"https://aidevboard.com/market-demand-pack?qc=api-jobs-market-demand-pack\u0026utm_campaign=skills_demand_pack\u0026utm_medium=jobs_api\u0026utm_source=api","name":"AI Market Demand Pack","next_step":"Open checkout_url for Stripe Checkout, or call api_checkout_url to get the non-charging checkout handoff payload.","price_usd":29,"product_id":"aidevboard_ai_skills_demand_pack","quote_url":"https://aidevboard.com/api/v1/quote?product_id=aidevboard_ai_skills_demand_pack"},"page":1,"per_page":20,"total":1747,"total_pages":88}