{"has_next":true,"jobs":[{"id":"a6cf2026-eaea-495b-8177-860a11bedb45","company_id":"168d43fe-0922-420c-9743-59e0a899fd9d","title":"Data Scientist","slug":"data-scientist-24024c78","description":"A Career with Point72’s Technology Team\n As Point72 reimagines the future of investing, our Technology team is constantly evolving our firm’s IT infrastructure and engineering capabilities, positioning us at the forefront of a rapidly evolving technology landscape. We’re a team of experts who experiment and work to discover new ways to harness open-source solutions, modern cloud architectures, and sophisticated Artificial Intelligence (AI) solutions, while embracing enterprise agile methodologies. Our commitment to building and innovating in the AI space provides the framework intended to drive smarter decision making and enhance how we build and operate our platforms and applications.\n As a member of Point72’s Technology team, we encourage and support your professional development from day one—helping you advance your technical skills, contribute innovative ideas, and satisfy your own intellectual curiosity—all while delivering real business impact for our multi-billion-dollar global business.\n  \n What you’ll do\n \n Lead the development and deployment of advanced models and algorithms that turn complex data into actionable insights to influence decisions across the organization\n Build and champion the rollout of a technology insights product, setting clear service standards, aligning stakeholders, and establishing transparent metrics to measure impact and drive adoption\n Design and maintain a centralized analytics platform that unifies key performance indicators, satisfaction scores, and operational metrics into intuitive dashboards for leadership\n Develop automated data pipelines and validation processes to gather, clean, and prepare large sets of structured and unstructured data for modeling and analysis\n Partner with data engineers, analysts, and business partners to translate business challenges into scalable, production-ready data solutions and shared standards\n Create reports and drill-down analyses that highlight service health, enable targeted action planning, and support proactive management\n Monitor and analyze performance across service quality, project manager satisfaction, efficiency, operational risk, and cost, highlighting trade-offs and providing strategic recommendations\n Use historical trend analysis and experimentation to uncover recurring issues, measure the impact of corrective actions, and drive continuous improvement\n Integrate third-party data sources and application programming interfaces into the analytics ecosystem to expand capabilities and enrich models\n Explore and implement modern cloud-native and distributed computing tools and methodologies to improve scalability, reliability, and reproducibility\n \n  \n What’s required\n \n 5–10 years of professional experience in data science or a closely related field in financial services or technology environments\n Bachelor's or master's degree in computer science, data science, statistics, engineering, or a related technical discipline\n Deep expertise in statistical modeling, machine learning, and data mining using Python, R, or similar programming languages\n Demonstrable experience with cloud-based analytics platforms, such as Amazon Web Services (AWS), and distributed computing frameworks, such as Spark or Databricks\n Strong skills in data wrangling, feature engineering, data quality management, and production data pipeline design\n Experience designing and implementing performance management systems, dashboards, or service excellence frameworks that inform leadership decisions\n Solid understanding of data architecture, data governance, reproducible research practices, and model monitoring in production\n Experience with version control systems—such as Git—continuous integration and delivery workflows, and modern workflow orchestration tools\n Proven ability to communicate complex analyses clearly to technical and non-technical stakeholders and to collaborate effectively in fast-paced, high-stakes environments\n Commitment to the highest ethical standards\n \n  \n We take care of our people\n We invest in our people, their careers, their health, and their well-being. When you work here, we provide:\n \n Fully-paid health care benefits\n Generous parental and family leave policies\n Volunteer opportunities\n Support for employee-led affinity groups representing women, people of color and the LGBT+ community\n Mental and physical wellness programs\n Tuition assistance\n A 401(k) savings program with an employer match and more\n \n  \n About Point72\n Point72 is a leading global alternative investment firm led by Steven A. Cohen. Building on more than 30 years of investing experience, Point72 seeks to deliver superior returns for its investors through fundamental and systematic investing strategies across asset classes and geographies. We aim to attract and retain the industry’s brightest talent by cultivating an investor-led culture and committing to our people’s long-term growth. For more information, visit  https://point72.com","salary_min":200000,"salary_max":300000,"location":"New York, NY","workplace":"onsite","job_type":"full-time","experience_level":"principal","tags":["distributed-systems","data-pipeline","mlops","data-science"],"apply_url":"https://boards.greenhouse.io/point72/jobs/8568268002?gh_jid=8568268002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T16:17:45Z","expires_at":"2026-06-29T14:11:58.685291Z","created_at":"2026-05-30T14:11:58.799327Z","updated_at":"2026-05-30T14:11:58.799327Z","company_name":"Point72","company_slug":"point72","company_logo_url":"https://www.google.com/s2/favicons?domain=point72.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a6cf2026-eaea-495b-8177-860a11bedb45"},{"id":"73600478-6692-47ce-be77-2aebfb5bb4a2","company_id":"82d2abc2-444c-4d89-9646-4739e72d700d","title":"Machine Learning Engineer","slug":"machine-learning-engineer-5aefaff6","description":"About Checkr Checkr is building the data platform to power safe and fair decisions. Over 140,000 companies and millions of people rely on Checkr for AI verification in the moments that matter most: getting a new job, a new place to live, a car ride, childcare, even a date. Customers include Uber, Pennymac, Airbnb, Doordash, Amazon, and Anthropic. We’re a team that thrives on solving complex problems with innovative solutions that advance our mission. Checkr is recognized on Forbes Cloud 100 2025 List and is a Y Combinator 2024 Breakthrough Company .\n About the team/role \n We’re hiring an ML Engineer (P2) to build and ship the AI systems that power Checkr’s core products. This role sits on the ML team inside Checkr’s Data \u0026 ML organization within Engineering.\n Checkr runs millions of background checks a year. The ML team builds the systems that make those checks faster, more accurate, and cheaper to operate: document processing, charge classification, entity resolution, and in-product intelligence. These are production services that Product Engineering depends on daily.\n This is not a research role or a notebook role. You’ll own ML services end-to-end: design them, code them, deploy them, monitor them. We need someone who writes production software, builds with LLMs and APIs as first-class tools, and can tell the difference between working code and AI slop. If you’ve spent the last few years building AI-native software and you care deeply about engineering craft, we want to talk.\n This role sits in the central Data \u0026 ML team within the Engineering organization. You will partner daily with Product Engineering, Product, and cross-functional teams. You’ll also contribute to Checkr’s broader AI strategy, including our initiative to deploy our agentic fleet and build scalable context with our semantic layer.\n We are looking for someone based in San Francisco who has built ML systems in fast-moving, impact-first environments. Less process, more shipping. Less paperwork, more results.\n  \n What you’ll do \n \n Build and deploy ML/AI services. Design, develop, and ship ML models and AI systems that Product Engineering teams rely on. You write the model code, the API layer, the monitoring, and the tests. Not notebooks; production services.\n Design with LLMs and APIs. Use LLM APIs (OpenAI, Anthropic, etc.) as building blocks in production systems. You know when to call an LLM, when to fine-tune, when to use a classical model, and when to write a rule. You think about cost, latency, and quality together.\n Ship production software. Write clean, well-structured code with solid OOP, proper abstractions, error handling, and tests. Your code gets reviewed by SWEs and passes. CI/CD is how you work, not something you bolt on at the end.\n Partner with product and engineering. Translate business problems into ML solutions. Define API contracts with product engineers. Explain your approach clearly to non-ML partners and leave the room with alignment, not confusion.\n Evaluate and iterate fast. Build evaluation frameworks, run experiments, and make data-driven decisions about model and system performance. Ship and iterate; don’t wait for perfect.\n Ship AI-powered workflows. Put AI to work on your own processes: automate pipelines, build agentic workflows, and contribute reusable skills and context to Checkr’s agentic platform. The expectation is that our teams operate AI-first.\n \n What you bring \n \n A Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related technical field, or equivalent depth from experience\n 4+ years building software professionally, with at least 2 of those building ML systems that run in production\n Strong Python fluency; you write clean, testable, well-structured code with solid OOP instincts. Not scripts; software\n Hands-on experience using LLM APIs in production systems: prompt engineering, structured outputs, function calling, cost management, and evaluation\n You’ve built and maintained APIs, worked with CI/CD pipelines, and shipped code that other engineers depend on\n Comfortable with distributed systems concepts: queues, async processing, caching, horizontal scaling\n Experience with NLP tasks in production: classification, extraction, entity resolution, summarization\n Comfort with and enthusiasm for AI-assisted workflows; experience using LLMs, code-generation tools, or agentic systems in production or operational contexts is a strong signal\n You can evaluate tradeoffs: fine-tune vs. prompt, hosted vs. self-deployed, classical ML vs. LLM, rule vs. model\n Strong communication skills; you explain technical decisions clearly to engineers and non-engineers alike, without hiding behind jargon\n You use AI tools (Copilot, Claude, etc.) to move faster, but you understand every line they produce. You can spot AI slop and you don’t ship it\n An A-player mindset with a strong bias for action: you raise the bar, move with urgency, stay resilient through ambiguity, and t","salary_min":168000,"salary_max":198000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"mid","tags":["nlp","code-generation","mlops","agents","payments","legal","distributed-systems","llm"],"apply_url":"https://job-boards.greenhouse.io/checkr/jobs/7966920","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T15:17:56Z","expires_at":"2026-06-29T14:10:31.076983Z","created_at":"2026-05-30T14:10:31.19215Z","updated_at":"2026-05-30T14:10:31.19215Z","company_name":"Checkr","company_slug":"checkr","company_logo_url":"https://www.google.com/s2/favicons?domain=checkr.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/73600478-6692-47ce-be77-2aebfb5bb4a2"},{"id":"530f705a-007a-497f-9f62-9a6e196ea9ad","company_id":"1df860e2-0800-48ea-81af-7121965be17a","title":"Engineering Manager - Machine Learning","slug":"engineering-manager-machine-learning-e1742de5","description":"Your work will change lives. Including your own. \n \n The Impact You’ll Make \n You will lead a team working to build, scale, and optimize the machine learning infrastructure that powers Recursion's drug discovery platform. From model training pipelines to production deployment systems, to agent infrastructure and Large Language Models, you will ensure our ML models can operate at massive scale across our supercomputing infrastructure, both on prem and in the cloud. You will work cross-functionally across ML engineering, data science, and research teams to translate requirements into robust, scalable ML infrastructure solutions.\n In This Role You Will: \n \n Enable AI/ML, LLM, and Agentic Systems teams for scale - The ML infrastructure team is responsible for building and operating platforms that allow data scientists and ML engineers to train, deploy, and monitor models across Recursion's massive datasets. With billions of compounds, 30+ petabytes of experimental data, and complex deep learning workloads, your team enables everything from automated compound screening models to clinical trial prediction systems. You will work closely with researchers and ML engineers to understand their infrastructure needs and build scalable solutions for model development, training, and deployment.\n Act as a mentor, coach, and sponsor - You will share your technical, leadership and managerial skills in MLOps, distributed computing, and infrastructure engineering, delivering impact, learning, and growth across teams at Recursion. We believe that the best work comes from working across organizational boundaries and you will have opportunities to partner with ML research, platform engineering, and business teams.\n Enable a model-driven culture - Machine learning is at the core of everything we do. You will work with stakeholders across the business to ensure our ML infrastructure supports rapid experimentation, reliable model deployment, and continuous improvement. Problems you will work on could range from optimizing GPU cluster utilization to implementing Agentic orchestration and establishing company-wide MLOps standards\n \n The Team You’ll Join: \n You'll be part of a group of technical leaders who work together on the craft of engineering leadership as well as debate ML system architecture, MLOps patterns, and infrastructure optimization strategies. We all work better when we have the support of those around us and are learning together to solve complex problems around model scalability, deployment reliability, and infrastructure efficiency across our teams. You will report to the Executive Director of Engineering who broadly oversees Cloud Infrastructure, High Performance Compute and Machine Learning Infrastructure space.\n The Experience You Will Need: \n \n Experience in a hands-on technical role as a tech lead or a manager with a focus on infrastructure, MLOps and distributed systems. Excitement for deeply engaging in technical details with your team around machine learning, orchestration and agentic systems.\n A people-first mindset. We deliver in a way that prioritizes supporting our coworkers in their growth and experience and understand how Conway's Law shapes our ML system outcomes.\n Demonstrated past record of learning from and teaching peers in areas of ML infrastructure, model deployment, distributed compute, GPU optimization, and MLOps system architecture\n Excitement to learn parts of our ML tech stack that you might not already know. Our current ML infrastructure includes: Python, PyTorch, Docker, Kubernetes, Ray, Weights \u0026 Biases, Prefect, BigQuery, Postgres, GCP, CUDA, and various model serving frameworks.\n Fluency in life sciences or drug discovery is a plus but not required to be considered.\n \n Working Location \u0026 Compensation: \n This is a remote position based in Toronto, Canada. \n At Recursion, we believe that every employee should be compensated fairly. Based on the skill and level of experience required for this role, the estimated current annual base range for this role is $210,070 to $282,851 (CAD) . You will also be eligible for an annual bonus and equity compensation, as well as a comprehensive benefits package. \n #LI-EP1\n The Values We Hope You Share: \n \n We act boldly with integrity. We are unconstrained in our thinking, take calculated risks, and push boundaries, but never at the expense of ethics, science, or trust. \n We care deeply and engage directly. Caring means holding a deep sense of responsibility and respect - showing up, speaking honestly, and taking action.\n We learn actively and adapt rapidly. Progress comes from doing. We experiment, test, and refine, embracing iteration over perfection.\n We move with urgency because patients are waiting. Speed isn’t about rushing but about moving the needle every day.\n We take ownership and accountability. Through ownership and accountability, we enable trust and autonomy—leaders take accountability for decisive action, and teams own outcomes ","salary_min":210070,"salary_max":282851,"location":"Toronto, Canada","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["distributed-systems","agents","mlops","gpu","healthcare","deep-learning","pytorch","llm"],"apply_url":"https://job-boards.greenhouse.io/recursionpharmaceuticals/jobs/7961536","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T14:56:14Z","expires_at":"2026-06-29T14:07:04.607932Z","created_at":"2026-05-30T14:07:04.722791Z","updated_at":"2026-05-30T14:07:04.722791Z","company_name":"Recursion","company_slug":"recursion","company_logo_url":"https://www.google.com/s2/favicons?domain=recursion.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/530f705a-007a-497f-9f62-9a6e196ea9ad"},{"id":"58a5e82c-4cbd-49e1-9ce2-45a7b213b0a9","company_id":"1df860e2-0800-48ea-81af-7121965be17a","title":"Engineering Manager - Machine Learning","slug":"engineering-manager-machine-learning-288c8ba8","description":"Your work will change lives. Including your own. \n \n The Impact You’ll Make \n You will lead a team working to build, scale, and optimize the machine learning infrastructure that powers Recursion's drug discovery platform. From model training pipelines to production deployment systems, to agent infrastructure and Large Language Models, you will ensure our ML models can operate at massive scale across our supercomputing infrastructure, both on prem and in the cloud. You will work cross-functionally across ML engineering, data science, and research teams to translate requirements into robust, scalable ML infrastructure solutions.\n In This Role You Will: \n \n Enable AI/ML, LLM, and Agentic Systems teams for scale - The ML infrastructure team is responsible for building and operating platforms that allow data scientists and ML engineers to train, deploy, and monitor models across Recursion's massive datasets. With billions of compounds, 30+ petabytes of experimental data, and complex deep learning workloads, your team enables everything from automated compound screening models to clinical trial prediction systems. You will work closely with researchers and ML engineers to understand their infrastructure needs and build scalable solutions for model development, training, and deployment.\n Act as a mentor, coach, and sponsor - You will share your technical, leadership and managerial skills in MLOps, distributed computing, and infrastructure engineering, delivering impact, learning, and growth across teams at Recursion. We believe that the best work comes from working across organizational boundaries and you will have opportunities to partner with ML research, platform engineering, and business teams.\n Enable a model-driven culture - Machine learning is at the core of everything we do. You will work with stakeholders across the business to ensure our ML infrastructure supports rapid experimentation, reliable model deployment, and continuous improvement. Problems you will work on could range from optimizing GPU cluster utilization to implementing Agentic orchestration and establishing company-wide MLOps standards\n \n The Team You’ll Join: \n You'll be part of a group of technical leaders who work together on the craft of engineering leadership as well as debate ML system architecture, MLOps patterns, and infrastructure optimization strategies. We all work better when we have the support of those around us and are learning together to solve complex problems around model scalability, deployment reliability, and infrastructure efficiency across our teams. You will report to the Executive Director of Engineering who broadly oversees Cloud Infrastructure, High Performance Compute and Machine Learning Infrastructure space.\n The Experience You Will Need: \n \n Experience in a hands-on technical role as a tech lead or a manager with a focus on infrastructure, MLOps and distributed systems. Excitement for deeply engaging in technical details with your team around machine learning, orchestration and agentic systems.\n A people-first mindset. We deliver in a way that prioritizes supporting our coworkers in their growth and experience and understand how Conway's Law shapes our ML system outcomes.\n Demonstrated past record of learning from and teaching peers in areas of ML infrastructure, model deployment, distributed compute, GPU optimization, and MLOps system architecture\n Excitement to learn parts of our ML tech stack that you might not already know. Our current ML infrastructure includes: Python, PyTorch, Docker, Kubernetes, Ray, Weights \u0026 Biases, Prefect, BigQuery, Postgres, GCP, CUDA, and various model serving frameworks.\n Fluency in life sciences or drug discovery is a plus but not required to be considered.\n \n Working Location \u0026 Compensation: \n This is an office-based, hybrid position at our US headquarters located in Salt Lake City, Utah . Employees are expected to work in the office at least 50% of the time.\n At Recursion, we believe that every employee should be compensated fairly. Based on the skill and level of experience required for this role, the estimated current annual base range for this role is $151,130 to $203,490 (USD) . You will also be eligible for an annual bonus and equity compensation, as well as a comprehensive benefits package. \n #LI-EP1\n The Values We Hope You Share: \n \n We act boldly with integrity. We are unconstrained in our thinking, take calculated risks, and push boundaries, but never at the expense of ethics, science, or trust. \n We care deeply and engage directly. Caring means holding a deep sense of responsibility and respect - showing up, speaking honestly, and taking action.\n We learn actively and adapt rapidly. Progress comes from doing. We experiment, test, and refine, embracing iteration over perfection.\n We move with urgency because patients are waiting. Speed isn’t about rushing but about moving the needle every day.\n We take ownership and accountability. Through ownership and acco","salary_min":151130,"salary_max":203490,"location":"Salt Lake City, Utah","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["pytorch","deep-learning","cloud","mlops","gpu","llm","distributed-systems","healthcare"],"apply_url":"https://job-boards.greenhouse.io/recursionpharmaceuticals/jobs/7961460","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T14:56:13Z","expires_at":"2026-06-29T14:07:04.532978Z","created_at":"2026-05-30T14:07:04.642889Z","updated_at":"2026-05-30T14:07:04.642889Z","company_name":"Recursion","company_slug":"recursion","company_logo_url":"https://www.google.com/s2/favicons?domain=recursion.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/58a5e82c-4cbd-49e1-9ce2-45a7b213b0a9"},{"id":"a944334e-23f0-4033-b1c8-307c9e7c7124","company_id":"75dcf7c0-5121-45f1-8d1b-6bfbfe15072f","title":"Helix AI Engineer, Backend Infrastructure ","slug":"helix-ai-engineer-backend-infrastructure-13269072","description":"Figure is an AI Robotics company developing a general purpose humanoid. Our humanoid robot is designed for commercial tasks and the home. We are based in San Jose and require 5 days/week in-office collaboration. It’s time to build.\n We're looking for a senior-level backend engineer who has scaled high-throughput, low-latency data systems and has strong instincts around cloud infrastructure and real-time streaming pipelines. You'll architect and build the core backend systems that power Figure's real-time data infrastructure — enabling the scale and reliability that our AI and robotics platforms depend on.\n This is a high-ownership role at the intersection of media and sensor data streaming, cloud systems, and applied ML serving. You'll work closely with our AI and robotics teams to ensure latency, reliability, and throughput meet the demands of real-world robot operation.\n WHAT YOU'LL DO \n \n Architect and scale cloud backend infrastructure for high-concurrency, real-time streaming of media and sensor data across robot fleets and user sessions.\n Design and build low-latency data pipelines that ingest, route, and process high-bandwidth streams — including camera feeds, IMU data, and other robot sensor outputs — into our AI stack in real time.\n Own reliability, latency, and throughput SLAs for streaming and data infrastructure.\n Collaborate with AI and robotics teams to integrate ML model serving into real-time data pipelines.\n Build observability, alerting, and tooling to give the team full situational awareness over live robot traffic.\n Drive architectural decisions and mentor engineers across the team.\n \n WHAT WE'RE LOOKING FOR \n \n Deep experience scaling cloud backend systems handling high-concurrency, real-time data streams — media, sensor, telemetry, or equivalent high-bandwidth pipelines.\n Strong fundamentals in distributed systems: stream processing, connection management, data transport, and low-latency architecture.\n Proficiency in one or more backend languages (Go, C++, Python, Rust) and cloud platforms (AWS, GCP, or Azure).\n Experience with containerized infrastructure, service mesh, and large-scale deployment pipelines.\n Strong communication and cross-functional collaboration skills.\n \n NICE TO HAVE \n \n Hands-on experience integrating AI inference serving (Triton Inference Server, TensorRT, SageMaker, or similar) into real-time data pipelines.\n Background in robotics, autonomous vehicles, live media platforms, or other latency-critical streaming domains.\n Familiarity with protocols such as WebRTC, RTSP, gRPC, or Kafka for real-time data transport.\n Experience with on-device or edge inference and the tradeoffs of cloud vs. edge processing.\n \n The US base salary range for this full-time position is between $150,000 - $400,000 annually.\n The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.","salary_min":150000,"salary_max":400000,"location":"San Jose, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["robotics","mlops","api-design","autonomous-vehicles","cloud","gpu","data-pipeline","distributed-systems"],"apply_url":"https://job-boards.greenhouse.io/figureai/jobs/4685172006","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-28T21:42:25Z","expires_at":"2026-06-29T14:05:53.514412Z","created_at":"2026-05-29T14:18:08.491663Z","updated_at":"2026-05-30T14:05:53.629497Z","company_name":"Figure AI","company_slug":"figure-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=figure.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a944334e-23f0-4033-b1c8-307c9e7c7124"},{"id":"b2263952-2d61-4a59-acd2-4d8506c9b16e","company_id":"cf6855a4-6591-475f-be10-f3f36cf31758","title":"Senior Software Engineer, Search Relevance","slug":"senior-software-engineer-search-relevance-8f221ba2","description":"WHAT IS BOX?  \n Box (NYSE:BOX) is the leader in Intelligent Content Management. Our platform enables organizations to fuel collaboration, manage the entire content lifecycle, secure critical content, and transform business workflows with enterprise AI. We help companies thrive in the new AI-first era of business. Founded in 2005, Box simplifies work for leading global organizations, including JLL, Morgan Stanley, and Nationwide. Box is headquartered in Redwood City, CA, with offices across the United States, Europe, and Asia.\n By joining Box, you will have the unique opportunity to continue driving our platform forward. Content powers how we work. It’s the billions of files and information flowing across teams, departments, and key business processes every single day: contracts, invoices, employee records, financials, product specs, marketing assets, and more. Our mission is to bring intelligence to the world of content management and empower our customers to completely transform workflows across their organizations. With the combination of AI and enterprise content, the opportunity has never been greater to transform how the world works together and at Box you will be on the front lines of this massive shift.\n WHY BOX NEEDS YOU \n The Search Relevance team at Box powers discovery across billions of files, enabling customers to find the right content quickly, securely, and intelligently. As we expand into a new era of AI-powered content understanding, we’re investing in the foundation that makes great search possible: reliable systems, strong signals, and models that learn from real-world usage.\n This is a rare opportunity to work at the intersection of information retrieval science, applied machine learning, and large-scale distributed systems. You’ll be building the infrastructure that powers intelligent content discovery for Fortune 500 companies—where milliseconds matter, relevance is measurable, and your experiments directly impact how millions of users work.\n We’re looking for a Senior Software Engineer to elevate search quality end-to-end—signals, ranking, retrieval, and evaluation—while building scalable, low-latency services that serve queries in real time. You’ll partner with Product, Data, and Infra teams to productionize cutting-edge models and experimentation frameworks, and help define the future of Box’s content intelligence, including hybrid and semantic search and our next-generation content agent.\n WHAT YOU'LL DO  \n \n Build and improve ranking, retrieval, and recommendation systems; identify the right signals and metrics to drive quality improvements that users can feel.\n Apply cutting-edge techniques (embeddings, LLM-enabled retrieval, hybrid search) to productionize experimentation and evaluation pipelines that scale to trillions of documents.\n Define and execute offline/online evaluation, A/B testing, and relevance tuning (NDCG, MRR, precision@k) to continuously improve search outcomes.\n Develop infrastructure for low-latency, high-availability query serving and near real-time indexing across distributed systems.\n Tackle distributed systems challenges including data sharding, intelligent routing, replication, and performance optimization.\n From ETL pipelines and feature engineering to model serving and result ranking—understand how data flows through the system and optimize at every stage.\n Lead design and implementation of new platform components from the ground up; establish patterns, raise the bar on code quality, and champion best practices.\n Share your expertise, contribute to technical direction, conduct thoughtful code reviews, and help shape our engineering culture.\n Participate in our on-call rotation, available at all times while on-call to help respond to and triage any issues that arise.\n \n WHO YOU ARE  \n \n 5+ years of industry experience building and operating backend or distributed systems at scale.\n Strong proficiency in an object-oriented language (e.g., Java, Scala, C++, or Python); Python experience strongly preferred.\n Hands-on experience building ranking, recommendation, NLP, or applied AI platforms in production. You understand the ML lifecycle from training to serving.\n Comfortable with data pipelines, message queues, and/or streaming systems (e.g., Kafka, Pub/Sub) and near real-time data processing.\n Experienced deploying and operating microservices in cloud environments; solid grasp of reliability, observability, and performance best practices.\n BS in Computer Science or related field, or equivalent practical experience.\n AI-first mindset—pragmatic about using the right models, signals, and evaluation methods to improve outcomes quickly and measurably.\n \n PREFERRED \n \n Experience with Elasticsearch, Solr, Lucene, or building custom search systems; deep understanding of inverted indexes, scoring functions, and query optimization.\n Knowledge of ML relevance tuning, learning-to-rank, retrieval evaluation metrics, offline/online testing, and A","salary_min":198500,"salary_max":248000,"location":"Redwood City, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["search","tensorflow","distributed-systems","pytorch","llm","nlp","fine-tuning","mlops"],"apply_url":"https://job-boards.greenhouse.io/boxinc/jobs/7926452","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-28T19:12:52Z","expires_at":"2026-06-29T14:19:20.83221Z","created_at":"2026-05-29T15:11:42.002134Z","updated_at":"2026-05-30T14:19:20.940887Z","company_name":"Box","company_slug":"box","company_logo_url":"https://www.google.com/s2/favicons?domain=box.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/b2263952-2d61-4a59-acd2-4d8506c9b16e"},{"id":"64170ac3-3bc0-4e64-aa55-14d395814525","company_id":"2ca4efa5-edc2-4352-a597-ea27086e1e5b","title":"Senior Machine Learning Engineer II, Ads Response Prediction","slug":"senior-machine-learning-engineer-ii-ads-response-prediction-c8a2de33","description":"We're transforming the grocery industry \n At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers. \n Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table.\n Instacart is a Flex First team \n There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. \n Overview \n As a Senior Machine Learning Engineer II on the Ads Response Prediction team, you will lead the design and development of core ML models that power Instacart’s ads ecosystem. This is a research-leaning role focused on theoretical problem formulation, training methodology, and model quality rather than infrastructure or full-stack engineering. You will tackle fundamental challenges in pCTR modeling such as mitigating selection bias, position bias, and optimizer’s curse in training data, improving model calibration across surfaces and domains, and advancing our multi-task learning and sequence modeling capabilities. You will also have the opportunity to shape our next-generation foundation model approach for ads ranking and contribute to cutting-edge retrieval systems like TIGER (Transformer Index for Generative Recommenders), Semantic ID and domain language models.\n The Ads Response Prediction team owns all systems, algorithms and ML models to ensure a relevant and engaging Ads experience to customers of all the platforms powered by Instacart. This includes search and exploration retrieval systems, sequential modeling and generative retrieval systems for next interaction recommendations, LLM integrations, relevance models, pCTR models, bidding models and incrementality models. The team optimizes for an efficient marketplace to ensure delightful customer shopping experience, desirable advertiser business outcome and Instacart Ads revenue.\n The team has strong ML infrastructure and MLOps support, including Delta/DBT-Spark data pipelines, Ray-based distributed training, and automated model deployment. This means you can focus your energy on advancing modeling science rather than building infrastructure.\n About the Job \n \n Lead research and development of pCTR and conversion prediction models, with a focus on improving calibration, reducing training data biases (selection bias, position bias, optimizer’s curse), and advancing model accuracy across Instacart’s ads surfaces.\n Design and implement debiasing techniques such as Mixed Negative Sampling (MNS), Inverse Propensity Weighting (IPW), counterfactual risk minimization, and calibration methods (Platt scaling, isotonic regression) to address systematic prediction biases.\n Contribute to the next-generation Multi-Domain Multi-Task (MDMT) model architecture, incorporating innovations like Mixture-of-Experts (MoE), Transformer layers for sequential user behavior, and LoRA adaptors for scalable domain fine-tuning.\n Drive sequence modeling initiatives including the TIGER generative retrieval system and Semantic ID representation learning, expanding their application across ads surfaces such as Product Details, Search and other placements.\n Collaborate with the broader ML community in the company on the path toward Foundation Models using autoregressive user behavior prediction.\n Formulate and scope ambiguous modeling problems from first principles. Translate business observations (e.g., overcalibration patterns, cold-start underperformance) into well-defined ML research directions with clear evaluation criteria.\n Publish and present findings internally. Contribute to the team’s culture of technical rigor through design reviews, paper sharing, and experiment retrospectives.\n \n About You \n Minimum Qualifications \n \n PhD/Master in machine learning, statistics, computer science, information retrieval, or a closely related quantitative field.\n 6+ years of combined academic and industry experience (including PhD research) applying ML to ranking, recommendation, or prediction problems at scale.\n Deep understanding of CTR/conversion prediction modeling, including familiarity with architectures such as Deep \u0026 Wide, DeepFM, DCN, and multi-task learning formulations.\n Strong foundation in causal inference, counterfactual reasoning, and","salary_min":201000,"salary_max":212000,"location":"Remote (US)","workplace":"remote","job_type":"full-time","experience_level":"senior","tags":["llm","pytorch","deep-learning","generative-ai","data-pipeline","mlops","fine-tuning","distributed-systems"],"apply_url":"https://instacart.careers/job/?gh_jid=7963838","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-28T19:10:27Z","expires_at":"2026-06-29T14:08:41.426586Z","created_at":"2026-05-29T14:32:35.186075Z","updated_at":"2026-05-30T14:08:41.541147Z","company_name":"Instacart","company_slug":"instacart","company_logo_url":"https://www.google.com/s2/favicons?domain=www.instacart.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/64170ac3-3bc0-4e64-aa55-14d395814525"},{"id":"ceb7845a-f491-495f-b9ad-afc4cbf8eff5","company_id":"3029e985-56bf-4ac2-9ae1-df4cdd53b12f","title":"Sr. Software Development Engineer-AI Security","slug":"sr-software-development-engineer-ai-security-12d276fe","description":"About Zscaler \n Zscaler accelerates digital transformation to ensure our customers can be more agile, efficient, resilient, and secure. As an AI-forward enterprise , we are constantly pushing the envelope, leveraging the world’s largest security data lake to power our cloud-native Zero Trust Exchange platform. This innovation protects our customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location.\n Here, impact in your role matters more than title and trust is built on results. We say, impact over activity. We seek innovators who actively use AI to amplify their impact and who thrive in an environment where we leverage intelligent systems to stay ahead of evolving threats. We believe in transparency and value constructive, honest debate —we’re focused on getting to the best ideas, faster. We build high-performing teams that can make an impact quickly and with high quality. To do this, we are building a culture of execution centered on customer obsession , collaboration, ownership, and accountability.\n We value high-impact, high-accountability with a sense of urgency where you’re enabled to do your best work and embrace your potential. If you’re driven by purpose, thrive on solving complex challenges, and want to be part of the team that’s helping to secure the AI age, we invite you to bring your talents to Zscaler and help shape the future of cybersecurity.\n Role \n We are looking for a Senior Software Development Engineer-AI Security to join us as a founding member of our AI Security Team. This is a Hybrid role based in San Jose, CA or Bellevue, WA (3 days in office), reporting to the Director of Software Engineering within the Emerging Tech org.\n You will build a high-reliability, low-latency AI security solution capable of scaling to hundreds of millions of users. In this role, you will be crucial in enhancing security capabilities for the AI within the world's largest cloud security platform by designing and implementing core infrastructure components and distributed systems while collaborating closely with stakeholders throughout the development lifecycle.\n What you’ll do (Role Expectations) \n \n Develop high-performance networking code for multiple desktop platforms using the Rust language and platform-native APIs\n Improve code quality through building solid, testable, and well-documented software foundations\n Design and implement major development projects with a focus on scalability, security, and performance\n Collaborate with product managers and cross-functional teams to deliver customer-impacting features\n Debug and solve complex network-related problems and enhance system functionality\n \n Who You Are (Success Profile) \n \n You thrive in ambiguity. You're comfortable building the path as you walk it. You thrive in a dynamic environment, seeing ambiguity not as a hindrance, but as the raw material to build something meaningful.\n You act like an owner. Your passion for the mission fuels your bias for action. You operate with integrity because you genuinely care about the outcome. True ownership involves leveraging dynamic range: the ability to navigate seamlessly between high-level strategy and hands-on execution.\n You are a problem-solver. You love running towards the challenges because you are laser-focused on finding the solution, knowing that solving the hard problems delivers the biggest impact.\n You are a high-trust collaborator. You are ambitious for the team, not just yourself. You embrace our challenge culture by giving and receiving ongoing feedback—knowing that candor delivered with clarity and respect is the truest form of teamwork and the fastest way to earn trust.\n You are a learner. You have a true growth mindset and are obsessed with your own development, actively seeking feedback to become a better partner and a stronger teammate. You love what you do and you do it with purpose.\n \n What We’re Looking for (Minimum Qualifications) \n \n Bachelor’s degree in computer science, engineering, or a related field\n 3+ years of software engineering experience with deep expertise in the Rust programming language and familiarity with lower-level languages such as C/C++\n Strong knowledge of system and network programming including firewalls, VPNs, protocols, TCP/IP, UDP, DNS, QUIC, H/3, and proxies\n Familiarity with system concepts such as virtual memory, multi-threading, and system APIs, and familiarity with SLM and LLM models\n Excellent debugging and problem-solving skills in both networking and system-level contexts\n \n What Will Make You Stand Out (Preferred Qualifications) \n \n Familiarity with DevOps pipelines, VPN technologies, and a strong understanding of security protocols and standards\n Experience writing testable, low-complexity code with dependency injection and thorough documentation\n Proficiency in additional programming languages like Swift, Python, or comparable technologies; direct experience in validating AI-d","salary_min":112000,"salary_max":160000,"location":"Bellevue, WA","workplace":"hybrid","job_type":"full-time","experience_level":"senior","tags":["mlops","data-pipeline","agents","security","llm","distributed-systems"],"apply_url":"https://job-boards.greenhouse.io/zscaler/jobs/5146134007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-28T14:55:45Z","expires_at":"2026-06-29T14:09:19.312884Z","created_at":"2026-05-29T14:33:11.44252Z","updated_at":"2026-05-30T14:09:19.425288Z","company_name":"Zscaler","company_slug":"zscaler","company_logo_url":"https://www.google.com/s2/favicons?domain=zscaler.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ceb7845a-f491-495f-b9ad-afc4cbf8eff5"},{"id":"bde15e9d-9623-47f7-a4e3-030d63ab1186","company_id":"57a9b50d-a69a-4f6f-9acb-910495c3c359","title":"Head of Marketing Operations","slug":"head-of-marketing-operations-0e090c3d","description":"About Us: \n At Fireworks, we’re building the future of generative AI infrastructure. Our platform delivers the highest-quality models with the fastest and most scalable inference in the industry. We’ve been independently benchmarked as the leader in LLM inference speed and are driving cutting-edge innovation through projects like our own function calling and multimodal models. Fireworks is a Series C company valued at $4 billion and backed by top investors including Benchmark, Sequoia, Lightspeed, Index, and Evantic. We’re an ambitious, collaborative team of builders, founded by veterans of Meta PyTorch and Google Vertex AI.\n About This Role \n The Head of Marketing Operations builds the infrastructure that lets Fireworks marketing scale, and runs the operating system that keeps the team performing day to day. You own the marketing tech stack, the data model, the lifecycle, and the analytics that turn marketing into a predictable pipeline engine that the executive team can trust. You also own the planning rhythms, budget, prioritization, and program management that keep every function inside marketing shipping on time and in sync.\n The right person is rigorous, opinionated about tooling, and energized by the operational problems most marketers avoid. You think about marketing the way a great operator thinks about a business: cadence, accountability, resource allocation, and clear measurement.\n Reports to: SVP Marketing Location: Remote (US) with periodic travel to San Mateo HQ Compensation: Competitive salary + equity\n Location and Work Style \n This role is remote-friendly within the US. You will travel to our San Mateo HQ periodically for team onsites, planning sessions, and key moments that benefit from being in person. We will establish a cadence that works for the team and the role.\n Responsibilities \n Marketing Technology Stack and Architecture \n You own the marketing tech stack end-to-end: selection, implementation, integration, and the standards that govern how data flows between systems. This includes the marketing automation platform, CDP or warehouse-native architecture decisions, enrichment, and the integration layer with Salesforce. Success is measured by stack reliability, total cost of ownership, and the speed at which marketing can launch new programs.\n Marketing Operating System and Program Management \n You run the operating rhythm of the marketing team. This includes the annual and quarterly planning process, goal setting and tracking, weekly business reviews, and cross-functional program management across demand gen, product marketing, content, and brand. You own the marketing budget model, vendor contracts, headcount planning support, and the prioritization framework that turns a long list of ideas into a focused roadmap. You are the connective tissue that makes the rest of the marketing team faster, more aligned, and easier to scale. Success is measured by on-time program delivery, budget accuracy, and team velocity.\n Lifecycle, Lead Management, and Scoring \n You own the full lifecycle from anonymous visitor through closed-won, including lead scoring, MQL and PQL definitions, SLA enforcement, and the handoff to sales. This includes the operational rigor around routing, nurture, and re-engagement. Success is measured by SDR conversion lift and clean handoff metrics.\n Attribution, Reporting, and Analytics \n You own marketing analytics, attribution methodology, and the dashboards that the executive team and the board see. This includes pipeline attribution, channel ROI, and the quarterly marketing performance review. Success is measured by leadership confidence in the numbers and by speed of decision-making informed by them.\n Data Governance and Compliance \n You own data hygiene, privacy compliance (GDPR, CCPA, and emerging US state laws), and the governance model that keeps our database trustworthy as we scale. Success is measured by data quality scores, deliverability rates, and zero material compliance incidents.\n What Success Looks Like \n \n Marketing-sourced pipeline is reported with confidence and audit-ready definitions\n Lead scoring drives measurable lift in downstream conversion rates\n The full funnel from visitor to closed-won is instrumented and visible in shared dashboards\n Campaign launch time drops as a result of better operational playbooks\n Marketing and sales agree on the data, the definitions, and the single source of truth\n The marketing team operates on a clear quarterly cadence with shared priorities, transparent budget, and on-time delivery against the plan\n The SVP of Marketing and the broader exec team have real-time visibility into team capacity, program status, and spend without chasing updates\n \n What This Role Does Not Own \n \n Campaign creative and execution: Demand Gen leader\n Sales technology and territory design: Sales Operations\n Product analytics and PLG instrumentation: Product and Data teams\n \n You Should Have \n \n 8+ years in marketing operations with ","salary_min":250000,"salary_max":280000,"location":"Remote (US)","workplace":"hybrid","job_type":"full-time","experience_level":"lead","tags":["agents","mlops","generative-ai","llm","pytorch"],"apply_url":"https://job-boards.greenhouse.io/fireworksai/jobs/4260883009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-27T22:28:00Z","expires_at":"2026-06-29T14:01:53.26087Z","created_at":"2026-05-28T14:02:31.996873Z","updated_at":"2026-05-30T14:01:53.369572Z","company_name":"Fireworks AI","company_slug":"fireworks-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=fireworks.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/bde15e9d-9623-47f7-a4e3-030d63ab1186"},{"id":"23d134c7-f2bf-4c83-87f8-3938851bc707","company_id":"e3915539-5a8f-4461-9f26-06366a918674","title":"Senior Machine Learning Engineer","slug":"senior-machine-learning-engineer-583097ba","description":"Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.\n ABOUT THE TEAM\n Anduril's Air \u0026 Missile Defense Radar team develops cutting-edge tracking algorithms and software systems that detect, track, and characterize airborne threats in real-time. We're building the next generation of tracking intelligence capabilities—automated analysis systems that understand tracking performance, identify failure modes, and continuously improve our algorithms through data-driven insights.\n This role sits at the intersection of ML engineering and tracking domain expertise. You'll build end-to-end pipelines that ingest tracking algorithm telemetry, analyze correlation failures and performance anomalies, train models to automate root cause analysis, and deploy production tools that help engineers ask questions like \"why didn't track X and track Y associate?\" We don't just track targets; we track our tracking systems and make them smarter.\n WHAT YOU'LL DO\n \n Own tracking intelligence infrastructure end-to-end : Build the platform for ingesting tracking algorithm telemetry (hypotheses, scores, gains, association decisions), feature engineering performance metrics, training analysis models, and deploying them into production\n Automate tracking analysis : Develop ML models that identify correlation failures, track quality degradation, and root causes for tracking anomalies—replacing manual deep-dive investigations with scalable automated insights\n Build autotuning capabilities : Create systems that recognize incoming data characteristics and automatically adjust tracking algorithm parameters, frame rates, and model configurations for optimal performance\n Design human-in-the-loop tools : Build interfaces and query services that let engineers ask natural questions about tracking behavior and get data-driven answers backed by your models\n Exploit tracking telemetry : Instrument C++ tracking algorithms with appropriate logging (working with platform engineers), then marshal that data into consistent formats for analysis and model training\n Deploy in constrained environments : Package and deploy models for air-gapped systems with no external connectivity, following security scanning requirements where ML models are treated as data artifacts\n Manage the ML lifecycle : Handle data catalogs, ground truth labeling, model registries, versioning, and validation—ensuring models improve tracking performance in measurable ways\n Bridge domains : Translate between tracking algorithm fundamentals (Kalman filters, data association, multi-hypothesis tracking) and ML/data science techniques to build solutions that actually work\n Drive make/build decisions : Evaluate when to build custom models vs. leverage existing ML capabilities, selecting appropriate algorithm architectures for tracking intelligence problems\n Work hands-on-keyboard : This is a one-person show initially—you'll architect, code, deploy, and iterate rapidly using modern Python-based ML tooling\n \n REQUIRED QUALIFICATIONS\n \n 3+ years of experience with a strong mix of ML engineering and data science—you've built models AND deployed them into production systems\n Proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, scikit-learn)\n Experience with MLOps practices: data pipelines, feature engineering, model versioning, experiment tracking, and deployment workflows\n Familiarity with ML infrastructure tooling (MLflow, Dagster/Airflow, or similar orchestration tools)\n Understanding of tracking, estimation, or filtering algorithms (Kalman filters, data association techniques)—you need to understand what tracking algorithms output and why they make the decisions they do\n Ability to work with streaming time-series data and engineer features from algorithm telemetry\n Experience building data catalogs, managing ground truth labels, and validating model performance\n Strong software engineering fundamentals—you can build maintainable, production-quality code independently\n Comfortable working in C++ environments enough to add instrumentation/logging (no deep algorithm development required)\n Ability to obtain and maintain a U.S. Top Secret SCI security clearance\n \n PREFERRED QUALIFICATIONS\n \n Experience deploying ML models in edge, embedded, or air-gapped environments with security constraints\n Background in def","salary_min":165000,"salary_max":218000,"location":"Fort Collins, CO","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["tensorflow","mlops","computer-vision","data-pipeline","pytorch","payments","machine-learning"],"apply_url":"https://boards.greenhouse.io/andurilindustries/jobs/5126634007?gh_jid=5126634007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-27T21:29:38Z","expires_at":"2026-06-29T14:06:48.665653Z","created_at":"2026-05-28T14:08:23.033047Z","updated_at":"2026-05-30T14:06:48.786007Z","company_name":"Anduril","company_slug":"anduril","company_logo_url":"https://www.google.com/s2/favicons?domain=anduril.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/23d134c7-f2bf-4c83-87f8-3938851bc707"},{"id":"41b3afd9-e8d0-4d82-9e8e-9149ad7c9147","company_id":"0bedcaf4-210e-4f52-95d5-a82be8aff446","title":"Sr Machine Learning Engineer, AI Research","slug":"sr-machine-learning-engineer-ai-research-866a2680","description":"Join the company that’s building the telemetry infrastructure for the AI era. At Cribl, we partner with IT and Security teams at many of the world’s biggest enterprises, including half of the Fortune 100, to bridge the gap between AI ambition and infrastructure reality. As the AI Platform for Telemetry, we give customers the choice, control, and flexibility to manage and analyze telemetry for both humans and agents, so they can build what’s next.\n We’re one of the fastest‑growing private companies and a leading player in a massive, fast‑moving market. With a global workforce, we’re remote‑first and grounded in a simple idea: software is a people business. Cribl is the place where curious, collaborative people can do their best work, grow fast, and bring their full selves to the herd.\n Why You'll Love This Role \n You will work closely with the founding team and a group of highly-skilled engineers to shape the future of AI-enabled Security/Observability platforms. You will play a central role in bringing integrating cutting-edge AI/ML technologies to the Cribl Product suite to help solve real customer problems.  You will work closely with development partners and key stakeholders to iteratively design, develop, and deliver products and surfaces that will delight our customers.\n On top of it all you will have fun. \n Cribl strives to be a great place to work for everyone.\n As An Active Member Of Our Team, You Will... \n \n Design, train, and evaluate machine learning models across a range of research and applied AI initiatives\n Run rapid, iterative experiments to test hypotheses and surface insights that drive model improvements\n Collaborate closely with researchers and engineers to translate cutting-edge academic advances into practical, production-ready systems\n Build and maintain robust ML pipelines for data ingestion, feature engineering, model training, and evaluation\n Optimize model performance through fine-tuning, hyperparameter search, and architecture experimentation\n Contribute to a culture of rigorous experimentation; tracking results, documenting findings, and sharing learnings with the broader team\n Stay current with the latest developments in ML and AI research, and proactively identify opportunities to apply them\n This position may require stand-by, on-call, or off-hours duties during critical research or deployment milestones\n \n If You've Got It - We Want It \n \n Bachelor's degree in Computer Science, Mathematics, Statistics, or a related field with 4+ years of industry or research experience (Master's or PhD a plus)\n Deep hands-on experience training and evaluating ML models, including language models\n Strong proficiency in Python and ML frameworks such as PyTorch or TensorFlow\n Familiarity with MLOps tooling and infrastructure (e.g., MLflow, Weights \u0026 Biases, Kubeflow, or similar)\n Solid understanding of modern NLP, computer vision, and/or reinforcement learning techniques\n Strong ability to move fast without sacrificing rigor; you know when to prototype and when to productionize\n Excellent communication skills with the ability to clearly present experimental results to both technical and non-technical stakeholders\n \n #LI-Tag #LI-Remote\n The salary for this role is dependent on geographic location and will be based on the individual candidate's job-related knowledge, skills, and experience. In addition to base salary, for sales and some sales-adjacent roles, employees are eligible to earn incentive compensation (commission). For all other roles, employees are eligible to participate in the Cribl Corporate Bonus Program. In addition to a competitive salary, Cribl also offers a generous benefits package which includes health, dental, vision, short-term disability, and life insurance, paid holidays and paid time off, a fertility treatment benefit, 401(k), and equity.\n Base Salary Range\n $185,000 — $215,000 USD \n Bring Your Whole Self Diversity drives innovation, enables better decisions to support our customers, and inspires change for the better. We’re building a culture where differences are valued and welcomed, and we work together to bring out the best in each other. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or any other applicable legally protected characteristics in the location in which the candidate is applying. \n Interested in joining the Cribl herd? Learn more about the smartest, funniest, most passionate goats you’ll ever meet at cribl.io/about-us .","salary_min":185000,"salary_max":215000,"location":"Remote (US)","workplace":"remote","job_type":"full-time","experience_level":"senior","tags":["nlp","tensorflow","computer-vision","mlops","pytorch","reinforcement-learning","fine-tuning","research"],"apply_url":"https://cribl.io/job-detail/?gh_jid=5979543004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-27T18:02:31Z","expires_at":"2026-06-29T14:18:07.512926Z","created_at":"2026-05-28T14:19:42.491471Z","updated_at":"2026-05-30T14:18:07.623902Z","company_name":"Cribl","company_slug":"cribl","company_logo_url":"https://www.google.com/s2/favicons?domain=cribl.io\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/41b3afd9-e8d0-4d82-9e8e-9149ad7c9147"},{"id":"bb30dd7a-6328-49a9-8992-8ef7d074aff9","company_id":"9f42c3ea-cd86-472e-8b5e-d041b53f16bf","title":"Machine Learning Engineer II","slug":"machine-learning-engineer-ii-2a76ae0d","description":"Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest.\n On the Servicing ML team, you will build and improve machine learning and AI systems that automate customer operations such as disputes, returns, fraud, and chargebacks to make the best decisions for Affirm and our customers. You will work closely with experienced ML engineers, platform partners, and cross-functional stakeholders to take models from idea to prototype to production, and to keep them healthy with strong measurement and monitoring.\n  \n What you'll do \n - You will develop AI systems that automate dispute and chargeback handling using structured evidence and business logic, creating a better experience for our customers.\n - You will build models that automate refunds, getting money back to our customers faster.\n - You will build and maintain evidence extraction pipelines that process unstructured data using LLM-powered workflows to produce structured, actionable outputs.\n - You will prototype new modeling ideas, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls.\n - You will collaborate across Engineering, Servicing Operations, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences.\n  \n What we look for \n - You have a total of 2+ years of experience as a machine learning engineer\n - Strong Python skills and experience writing production-quality code\n - Experience building and evaluating models for tabular classification problems (preferably gradient-boosted decision trees like LightGBM/XGBoost/CatBoost).\n - Experience building applications with LLM APIs (e.g., OpenAI, Anthropic), including structured extraction, prompt engineering, and orchestration frameworks like LangChain or LangGraph.\n - Familiarity with document and unstructured data processing (PDF/image extraction, text parsing, or similar).\n - Experience with ML lifecycle tooling for training orchestration, experimentation, and model monitoring (e.g., Kubeflow, Airflow, MLflow, or equivalent internal platforms).\n - Proficient in using AI-powered developer tools (e.g., Claude Code, Cursor, or similar) to accelerate iteration, debugging, and code quality as part of day-to-day development workflows.\n - You have mastered taking a simple problem or business scenario into a solution that interacts with multiple software components, and executing on it by writing clear, easily understood, well tested and extensible code.\n - You are comfortable navigating a large code base, debugging others' code, and providing feedback to other engineers through code reviews.\n - Your experience demonstrates that you take ownership of your growth, proactively seeking feedback from your team, your manager, and your stakeholders.\n - You have strong verbal and written communication skills that support effective collaboration with our global engineering team.\n \n  \n  \n Pay Grade - L Equity Grade - 5 Employees new to Affirm typically come in at the start of the pay range. Affirm focuses on providing a simple and transparent pay structure which is based on a variety of factors, including location, experience and job-related skills.  Base pay is part of a total compensation package that may include monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents). In addition, the employees may be eligible for equity rewards offered by Affirm Holdings, Inc. (parent company). CAN base pay range per year: $125,000 - $175,000 \n Location - Remote Canada\n #LI Remote\n \n Affirm is proud to be a remote-first company! The majority of our roles are remote and you can work almost anywhere within the country of employment. Affirmers in proximal roles have the flexibility to work remotely, but will occasionally be required to work out of their assigned Affirm office. A limited number of roles remain office-based due to the nature of their job responsibilities.\n We’re extremely proud to offer competitive benefits that are anchored to our core value of people come first. Some key highlights of our benefits package include:  \n \n Health care coverage - Affirm covers all premiums for all levels of coverage for you and your dependents  \n Flexible Spending Wallets - generous stipends for spending on Technology, Food, various Lifestyle needs, and family forming expenses \n Time off - competitive vacation and holiday schedules allowing you to take time off to rest and recharge \n ESPP - An employee stock purchase plan enabling you to buy shares of Affirm at a discount \n \n We believe It’s On Us to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individuali","salary_min":125000,"salary_max":175000,"location":"Remote (Canada)","workplace":"remote","job_type":"full-time","experience_level":"junior","tags":["mlops","agents","llm","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/affirm/jobs/7719653003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-25T15:10:02Z","expires_at":"2026-06-29T14:17:59.886987Z","created_at":"2026-05-27T14:18:51.302019Z","updated_at":"2026-05-30T14:18:00.002246Z","company_name":"Affirm","company_slug":"affirm","company_logo_url":"https://www.google.com/s2/favicons?domain=affirm.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/bb30dd7a-6328-49a9-8992-8ef7d074aff9"},{"id":"9bb36624-5eb3-472c-87cb-a72da24480bc","company_id":"9f42c3ea-cd86-472e-8b5e-d041b53f16bf","title":"Machine Learning Engineer II","slug":"machine-learning-engineer-ii-24c947be","description":"Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest.\n On the Servicing ML team, you will build and improve machine learning and AI systems that automate customer operations such as disputes, returns, fraud, and chargebacks to make the best decisions for Affirm and our customers. You will work closely with experienced ML engineers, platform partners, and cross-functional stakeholders to take models from idea to prototype to production, and to keep them healthy with strong measurement and monitoring.\n  \n What you'll do \n - You will develop AI systems that automate dispute and chargeback handling using structured evidence and business logic, creating a better experience for our customers.\n - You will build models that automate refunds, getting money back to our customers faster.\n - You will build and maintain evidence extraction pipelines that process unstructured data using LLM-powered workflows to produce structured, actionable outputs.\n - You will prototype new modeling ideas, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls.\n - You will collaborate across Engineering, Servicing Operations, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences.\n  \n What we look for \n - You have a total of 2+ years of experience as a machine learning engineer\n - Strong Python skills and experience writing production-quality code\n - Experience building and evaluating models for tabular classification problems (preferably gradient-boosted decision trees like LightGBM/XGBoost/CatBoost).\n - Experience building applications with LLM APIs (e.g., OpenAI, Anthropic), including structured extraction, prompt engineering, and orchestration frameworks like LangChain or LangGraph.\n - Familiarity with document and unstructured data processing (PDF/image extraction, text parsing, or similar).\n - Experience with ML lifecycle tooling for training orchestration, experimentation, and model monitoring (e.g., Kubeflow, Airflow, MLflow, or equivalent internal platforms).\n - Proficient in using AI-powered developer tools (e.g., Claude Code, Cursor, or similar) to accelerate iteration, debugging, and code quality as part of day-to-day development workflows.\n - You have mastered taking a simple problem or business scenario into a solution that interacts with multiple software components, and executing on it by writing clear, easily understood, well tested and extensible code.\n - You are comfortable navigating a large code base, debugging others' code, and providing feedback to other engineers through code reviews.\n - Your experience demonstrates that you take ownership of your growth, proactively seeking feedback from your team, your manager, and your stakeholders.\n - You have strong verbal and written communication skills that support effective collaboration with our global engineering team.\n - This position requires either equivalent practical experience or a Bachelor’s degree in a related field\n  \n \n Base Pay Grade - L Equity Grade - 6\n Employees new to Affirm typically come in at the start of the pay range. Affirm focuses on providing a simple and transparent pay structure which is based on a variety of factors, including location, experience and job-related skills. Base pay is part of a total compensation package that may include equity rewards, monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents.) USA base pay range (CA, WA, NY, NJ, CT) per year: $160,000 - $210,000 USA base pay range (all other U.S. states) per year: $142,000 - $192,000 #LI-Remote\n  \n \n Affirm is proud to be a remote-first company! The majority of our roles are remote and you can work almost anywhere within the country of employment. Affirmers in proximal roles have the flexibility to work remotely, but will occasionally be required to work out of their assigned Affirm office. A limited number of roles remain office-based due to the nature of their job responsibilities.\n We’re extremely proud to offer competitive benefits that are anchored to our core value of people come first. Some key highlights of our benefits package include:  \n \n Health care coverage - Affirm covers all premiums for all levels of coverage for you and your dependents  \n Flexible Spending Wallets - generous stipends for spending on Technology, Food, various Lifestyle needs, and family forming expenses \n Time off - competitive vacation and holiday schedules allowing you to take time off to rest and recharge \n ESPP - An employee stock purchase plan enabling you to buy shares of Affirm at a discount \n \n We believe It’s On Us to provide an inclusive interview experience for all, including people with disabilities. We a","salary_min":142000,"salary_max":192000,"location":"Remote (US)","workplace":"remote","job_type":"full-time","experience_level":"junior","tags":["agents","mlops","llm","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/affirm/jobs/7719651003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-25T15:10:00Z","expires_at":"2026-06-29T14:17:59.968032Z","created_at":"2026-05-27T14:18:51.394226Z","updated_at":"2026-05-30T14:18:00.078113Z","company_name":"Affirm","company_slug":"affirm","company_logo_url":"https://www.google.com/s2/favicons?domain=affirm.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9bb36624-5eb3-472c-87cb-a72da24480bc"},{"id":"4ee3ad62-c834-40b6-9d23-94f8f18e413d","company_id":"6195a3ea-00dd-46bf-a128-51a98a52d538","title":"Senior Software Engineer, AI Platform","slug":"senior-software-engineer-ai-platform-cc64dd0b","description":"About Mixpanel \n Mixpanel turns data clarity into innovation. Trusted by more than 29,000 companies, including Workday, Pinterest, LG, and Rakuten Viber, Mixpanel’s AI-first digital analytics help teams accelerate adoption, improve retention, and ship with confidence. Powering this is an industry-leading platform that combines product and web analytics, session replay, experimentation, feature flags, and metric trees. Mixpanel delivers insights that customers trust. Visit mixpanel.com to learn more.\n About the Team \n The AI Platform team is a newly formed team at the center of Mixpanel's AI-first analytics vision. With a greenfield charter, we're building the infrastructure that accelerates and transforms AI product development at Mixpanel, both internally and for our customers. \n We provide the tools to improve AI products, enabling internal teams and customer agents to build things that were previously unimaginable.  We build shared infrastructure that is essential for developing AI features with confidence and speed, and that gives every agent the tools, context, and quality guarantees to act autonomously on behalf of users.\n Some examples of what we are building:\n \n Agent Optimization Framework : A measurement and tuning system for Mixpanel AI that uses evals and metrics from production to optimize for speed, quality, and cost across a wide range of use cases.\n AI Agent Integrations and Accessibility : Products and tools that bring the power of Mixpanel to wherever it is most effective for our customers, including a Mixpanel slackbot and a public skills library.\n Automatic Generation of AI Inputs: Infrastructure to provide input to LLMs (e.g. context windows) with relevant customer and behavioral data that elevates agent performance.\n \n Role Overview \n We are looking for an experienced and driven Senior Software Engineer to join our AI Platform team. You will be responsible for building the scalable, secure, and reliable infrastructure that accelerates AI agent development.  You will be a leader and key contributor in a small, fast paced, newly formed team with a mandate to empower AI development across the company.\n Responsibilities \n \n Platform Architecture: Design and develop the core backend services, APIs, and microservices that enable product teams to easily and securely leverage AI models.\n Agent Orchestration: Build scalable frameworks and tools to support multi-step agent workflows, including task decomposition, tool invocation, and persistent memory.\n Evaluation \u0026 Reliability: Build robust evaluation systems to continuously measure reasoning quality, hallucination rates, and task success.\n Operational Excellence: Architect high-performance serving infrastructure with strict guarantees around latency, throughput, cost-efficiency, and error handling.\n Observability \u0026 MLOps: Ensure comprehensive monitoring, structured logging, and distributed tracing across all deployed AI models.\n Collaboration: Partner with designers, product managers, and other engineers to build self-serve infrastructure that transforms our AI development cycle.\n Leadership: Advocate for software engineering best practices, conduct thorough design and code reviews, and mentor junior engineers.\n \n We're Looking For Someone Who Has \n \n Bachelor's degree in Computer Science, Mathematics, a related field, or equivalent practical experience\n 5+ years of professional software engineering experience\n Strong full-stack fundamentals: you're comfortable working across frontend, backend, and data layers\n Excellent debugging and technical investigation skills\n Strong technical communication, ideally with experience collaborating in an asynchronous remote environment\n Ability to move fast and iterate in ambiguous environments: you take ownership and focus on delivering value to users\n Hands on experience integrating and orchestrating LLMs and agents\n Experience building AI native systems, including iteratively improving and scaling them in production\n A desire to be on the forefront of leveraging AI to drive product improvement, observability and product analytics at scale.\n \n  \n Compensation \n The amount listed below is the total target cash compensation (TTCC) and includes base compensation and variable compensation in the form of either a company bonus or commissions. Variable compensation type is determined by your role and level. In addition to the cash compensation provided, this position is also eligible for equity consideration and other benefits including medical, vision, and dental insurance coverage. You can view our benefits offerings here . Our salary ranges are determined by role and level and are benchmarked to the SF Bay Area Technology data cut released by Radford, a global compensation database. The range displayed represents the minimum and maximum TTCC for new hire salaries for the position across all of our US locations. To stay on top of market conditions, we refresh our salary ranges twice a year so these rang","salary_min":226000,"salary_max":306000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["agents","mlops","cloud","llm","microservices","platform"],"apply_url":"https://job-boards.greenhouse.io/mixpanel/jobs/7941944","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-23T14:02:17Z","expires_at":"2026-06-29T14:19:19.319071Z","created_at":"2026-05-27T14:20:15.169378Z","updated_at":"2026-05-30T14:19:19.429984Z","company_name":"Mixpanel","company_slug":"mixpanel","company_logo_url":"https://www.google.com/s2/favicons?domain=mixpanel.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/4ee3ad62-c834-40b6-9d23-94f8f18e413d"},{"id":"ffc677b3-ea58-4292-a9a8-ffc5cf009a40","company_id":"b467c425-56b3-40ce-826a-e603e82a08bd","title":"Senior Machine Learning Engineer, GenAI Data","slug":"senior-machine-learning-engineer-genai-data-0eedcadb","description":"Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators.  \n At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. 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 As a Senior Software Engineer  on the Foundation AI organization, you will sit at the epicenter of our foundation model efforts. While the research world is focused on architecture, you will be the architect of the data flywheel that makes VideoGen and 3DGen possible. You aren't just building pipelines; you are building the infrastructure that defines how our models perceive and generate virtual worlds in three dimensions and across time.\n In this role, you will partner directly with our AI researchers to advance beyond experimental datasets and into the realm of dynamic, high-fidelity data synthesis and evaluation. You will bridge the gap between research prototypes working locally to scaling for millions of users. You will design, implement, and scale robust, high-performance infrastructure to crawl, create, curate, store, and serve the massive datasets required for these models. We are seeking accomplished software engineers with a passion for data, experience building large distributed systems, and a commitment to writing high-quality, well-tested code to solve complex data challenges at scale. Your contributions will ensure that our foundation models receive the highest quality data, thereby supporting the next generation of creative AI.\n You will: \n \n High-Scale Data Orchestration: Architect and maintain automated pipelines for the ingestion, cleaning, and pre-processing of multi-modal datasets (video, 3D,) spanning petabytes of data\n Synthetic Data Generation: Leverage image and video generation models to scale multi-modal synthetic datasets\n Research-to-Production Bridge: Partner with research teams to create training data for research experiments – research and implement synthetic data creation pipelines\n Scalable Evaluation Frameworks: Build and own evaluation—automating both heuristic-based metrics and human-in-the-loop interfaces to evaluate and benchmark training datasets and in-house foundation models\n Model Deployment \u0026 API Architecture: Design and optimize high-throughput, low-latency Inference APIs for internal and external consumer access\n Autonomous SOTA Tracking: Actively participate in literature reviews and paper reading groups to identify and implement the latest optimizations in generative modeling\n Resource Efficiency \u0026 Observability: Implement monitoring pipeline health, optimizing data loading to ensure GPUs are used efficiently\n \n You have: \n \n 8+ years of experience as a research-focused data systems engineer (preferably working with 3D and video foundation models)\n Expertise in building scalable ML data pipelines for both batch and real-time environments. Experience working with and processing very large datasets (Petabytes or more).\n Versatile: You're a generalist and you are comfortable with several languages and technologies already; you are adaptable in any situation\n Team-Player \u0026 Technical Leader: You are a collaborative team member who actively mentors peers, drives technical excellence, and takes ownership of leading and delivering key features and projects across team boundaries\n Python Proficiency: You can write high-quality Python code for automation, tooling, and infrastructure management\n Experience with cloud data platforms and distributed processing technologies (e.g., Spark, Ray, Kubeflow, S3, etc.).\n Are passionate about the potential of generative AI, particularly in creative domains like 3D/4D content.\n A Bachelor's degree or equivalent experience in Computer Science, Computer Engineering, or a similar technical field\n \n You are:  \n \n MLOps Experience: Knowledge of experiment tracking (Weights \u0026 Biases, MLflow) and versioning for massive datasets.\n Custom Tooling Development: Experience building internal \"human-in-the-loop\" tools for data labeling specific to video or 3D.\n C++ Knowledge: Optimize the performance of data loaders and being comfortable modifying engine code.\n Game development and digital content creation tools : Experience with making Roblox games, using Blender, Unreal Engine, or Unity.\n  \n For roles that are based at our headquarters in San Mateo, CA: The starting base pay for this position is as shown below. The actual base pay is dependent upon a variet","salary_min":243290,"salary_max":295250,"location":"San Mateo, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["distributed-systems","generative-ai","mlops","data-pipeline","machine-learning"],"apply_url":"https://careers.roblox.com/jobs/7943933?gh_jid=7943933","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-21T23:44:43Z","expires_at":"2026-06-29T14:17:04.294884Z","created_at":"2026-05-27T14:17:52.923084Z","updated_at":"2026-05-30T14:17:04.408385Z","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/ffc677b3-ea58-4292-a9a8-ffc5cf009a40"},{"id":"3a790011-3259-4ddc-b03a-1e3227951d9b","company_id":"c587b06c-b6f0-4d1d-b694-6fb6abc2a6bb","title":"Forward Deployed Engineer","slug":"forward-deployed-engineer-988ebd0a","description":"Who We Are \n Lightning AI is the company behind PyTorch Lightning. Founded in 2019, we build an end-to-end platform for developing, training, and deploying AI systems—designed to take ideas from research to production with less friction.\n Through our merger with Voltage Park, a neocloud and AI Factory, Lightning AI combines developer-first software with cost-efficient, large-scale compute. Teams get the tools they need for experimentation, training, and production inference, with security, observability, and control built in.\n We serve solo researchers, startups, and large enterprises. Lightning AI operates globally with offices in New York City, San Francisco, Seattle, and London, and is backed by Coatue, Index Ventures, Bain Capital Ventures, and Firstminute.\n  \n What We Are Looking For \n We are seeking an experienced  Forward Deployed Engineer  to partner directly with customers to architect, build, and deploy production AI systems and workflows on Lightning AI’s platform. In this role, you will own the customer journey from early exploration through production deployment, translating ambiguous business goals into reliable, observable systems with clear quality, latency, scalability, and cost outcomes.\n This role sits at the intersection of software engineering, research engineering, AI infrastructure, product thinking, and customer engagement. You’ll work closely with customer engineering teams as well as Lightning’s internal product and engineering organizations to deliver production-ready AI systems that help customers realize value quickly and scale with confidence.\n This is a hands-on engineering role that combines software development, AI infrastructure, technical customer engagement, and product thinking. Successful candidates will be  highly technical, customer-oriented builders who thrive in fast-moving environments and enjoy solving ambiguous, real-world AI systems problems.\n This role is based in one of our hubs (New York City, San Francisco, Seattle, or London), with a minimum of 2 in-office days per week and occasional team and company offsites. \n What You'll Do \n \n Partner directly with customers to design, implement, and deploy end-to-end AI systems and workflows on Lightning’s platform\n Translate vague customer objectives into clear technical specifications, proof-of-concepts, and scalable production implementations\n Own customer technical engagements end-to-end, from early discovery and architecture through deployment, monitoring, and expansion\n Develop and maintain production-grade software systems and services using modern programming languages, with a strong preference for Python\n Build reliable, observable systems with strong attention to latency, throughput, quality, scalability, and cost efficiency in production environments\n Debug and optimize AI systems across inference infrastructure, model behavior, APIs, and distributed workloads to improve performance and reliability\n Work closely with customer engineering teams throughout the full lifecycle of AI deployments, including technical discovery, implementation, deployment, and scaling\n Collaborate cross-functionally with Lightning’s product and engineering teams to improve platform capabilities, influence roadmap priorities, and identify opportunities for reusable product improvements\n Navigate ambiguity with sound technical judgment, making thoughtful tradeoffs and selecting the right tools and approaches without introducing unnecessary complexity\n Demonstrate strong ownership and accountability in execution, with a commitment to delivering high-quality outcomes for both customers and internal teams\n \n What You’ll Need \n Required Qualifications \n \n Strong software engineering experience building and maintaining production systems in one or more general-purpose programming languages, with Python strongly preferred\n Experience working directly with customers in highly technical environments, such as Forward Deployed Engineering, Solutions Engineering, Applied AI Engineering, Technical Product Engineering, or related roles\n Familiarity with AI/ML pipelines and the lifecycle of model development, evaluation, deployment, and monitoring\n Experience deploying and operating production AI/ML systems in cloud or distributed environments\n Familiarity with modern AI infrastructure and tooling such as Docker, Kubernetes, APIs, model serving systems, or distributed inference workloads\n Strong communication and collaboration skills, especially when working through complex technical topics with customers, engineers, and cross-functional stakeholders\n Ability to translate business needs into technical solutions and drive projects from initial concept through production delivery\n Ability to execute effectively in ambiguous, fast-moving, high-growth environments\n Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field\n \n Nice-to-Haves \n \n Experience building, deploying, or optimizing large-scale AI/ML","salary_min":120000,"salary_max":250000,"location":"London, UK","workplace":"hybrid","job_type":"full-time","experience_level":"mid","tags":["distributed-systems","fine-tuning","pytorch","embeddings","search","llm","mlops"],"apply_url":"https://job-boards.greenhouse.io/lightningai/jobs/7742081003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-21T17:15:55Z","expires_at":"2026-06-29T14:03:02.726355Z","created_at":"2026-05-27T14:03:14.78242Z","updated_at":"2026-05-30T14:03:02.834329Z","company_name":"Lightning AI","company_slug":"lightning-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=lightning.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/3a790011-3259-4ddc-b03a-1e3227951d9b"},{"id":"789228a9-72bb-4a56-98ca-87b1968a76fd","company_id":"698abc6f-9497-4ea6-809f-f0f7c2788a46","title":"Staff GPU Systems Engineer, Space Computing","slug":"staff-gpu-systems-engineer-space-computing-bf7c9dd7","description":"At Relativity Space, we’re building rockets to serve today’s needs and tomorrow’s breakthroughs. Our Terran R vehicle will deliver customer payloads to orbit, meeting the growing demand for launch capacity. But that’s just the start. Achieving commercial success with Terran R will unlock new opportunities to advance science, exploration, and innovation, pioneering progress that reaches beyond the known. \n Joining Relativity means becoming part of something where autonomy, ownership, and impact exist at every level. Here, you're not just executing tasks; you're solving problems that haven’t been solved before, helping develop a rocket, a factory, and a business from the ground up. Whether you’re in propulsion, manufacturing, software, avionics, or a corporate function, you’ll collaborate across teams, shape decisions, and see your work come to life in record time. Relativity is a place where creativity and technical rigor go hand in hand, and your voice will help define the stories we’re writing together. Now is a unique moment in time where it’s early enough to leave your mark on the product, the process, and the culture, but far enough along that Terran R is tangible and picking up momentum. The most meaningful work of your career is waiting. Join us. \n  \n About the Team:  \n The Interplanetary Sciences Program was established  to expand access to scientific exploration across our solar system. Its mission is to make planetary research faster, more affordable, and more capable than ever before by rethinking how science missions are designed, built, and  operated . The program aims to enable scientists to send instruments to distant worlds without decades of development or prohibitive costs. By creating a sustainable model for interplanetary exploration, we are transforming space science from an occasional event into a continuous process of discovery that accelerates knowledge, broadens participation, and inspires the next generation of explorers.   \n About the Role: \n \n Own the GPU compute environment for a space-based data center — setup, driver integration, container runtime, job scheduling, and performance optimization — building the platform that enables onboard AI/ML inference and SAR reprocessing millions of miles from the nearest sysadmin \n Profile and optimize compute performance across the full stack: GPU utilization, memory bandwidth, I/O throughput, and storage interface performance, squeezing maximum science return from constrained power and thermal budgets that shift between sunlit burst processing and eclipse idle periods \n Build power and thermal-aware compute scheduling that orchestrates batch workloads around orbital constraints, coordinating with the storage platform to sustain 10 Gbps data movement between NAS and compute nodes during processing windows \n Develop compute health monitoring and upset recovery mechanisms — checkpoint/restart strategies, GPU fault detection, and automated recovery — so a radiation-induced upset means a restarted job, not a lost processing window \n Integrate GPU drivers with the payload Linux image in coordination with the Platform RE, manage the container runtime for compute workloads, and ensure the platform reliably runs ML frameworks and SAR processing pipelines maintained by the broader operations team \n \n About You: \n \n BS/MS in Computer Science or Electrical Engineering and 5+ years of relevant experience \n Hands-on experience with GPU programming and compute frameworks — CUDA, ROCm, or OpenCL — with real performance profiling and optimization work, not just running tutorials \n Strong Linux systems administration and performance tuning skills: you've diagnosed I/O bottlenecks, tuned memory management, and understood why a workload isn't hitting expected throughput \n Experience with container technologies (Docker, Podman, or lightweight alternatives) and HPC job scheduling concepts \n Working proficiency in Python for tooling, scripting, and ML framework integration, with C/C++ skills for performance-critical system components \n \n Nice to haves but not required:    \n \n Experience with HPC cluster administration, ML infrastructure, or cloud GPU compute platforms at scale \n Deep familiarity with ML framework runtime requirements — PyTorch or TensorFlow deployment, model serving, and inference optimization \n Knowledge of GPU compute architectures at the hardware level: CUDA cores, compute units, memory hierarchies, and how they affect real workload performance \n Experience with high-throughput data movement and storage I/O optimization — NFS tuning, buffer management, and sustaining multi-gigabit throughput \n Background in power-managed computing: duty cycling, thermal throttling, and workload scheduling under variable power constraints \n Experience designing checkpoint/restart or fault-tolerant batch processing systems — space experience not required, similar problems exist in large-scale distributed infrast","salary_min":181000,"salary_max":248500,"location":"Long Beach, California","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["mlops","pytorch","fine-tuning","gpu","tensorflow"],"apply_url":"https://boards.greenhouse.io/relativity/jobs/8560518002?gh_jid=8560518002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-21T17:01:37Z","expires_at":"2026-06-29T14:18:13.388673Z","created_at":"2026-05-27T14:19:05.177476Z","updated_at":"2026-05-30T14:18:13.506801Z","company_name":"Relativity","company_slug":"relativity","company_logo_url":"https://www.google.com/s2/favicons?domain=relativity.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/789228a9-72bb-4a56-98ca-87b1968a76fd"},{"id":"c5dcfa8b-bd45-4ef4-b917-e879853655e2","company_id":"28040a6c-6f94-41a4-b15a-f2e4520188ff","title":"AI Systems Engineer","slug":"founding-ai-systems-engineer-afa57fc7","description":"About Dialpad Dialpad is the AI-native business communications platform. We unify calling, messaging, meetings, and contact center on a single platform - powered by AI that understands every conversation in real time. \n More than 70,000 companies around the globe, including WeWork, Asana, NASDAQ, AAA Insurance, COMPASS Realty, Uber, Randstad, and Tractor Supply, rely on Dialpad to build stronger customer connections using real-time, AI-driven insights. \n We’re now leading the shift to Agentic AI: intelligent agents that don’t just analyze conversations but take action by automating workflows, resolving customer issues, and accelerating revenue in real time. Our DAART initiative (Dialpad Agentic AI in Real Time) is redefining what a communications platform can do. \n Visit dialpad.com to learn more. \n Being a Dialer At Dialpad, AI isn’t just a feature; it’s how our teams do their best work every day. We put powerful AI tools in every employee’s hands so they can move faster, think bigger, and achieve more. \n We believe every conversation matters. And we’ve built the platform that turns those conversations into insight and action, for our customers and ourselves. \n We look for people who are intensely curious and hold themselves to a high bar. Our ambition is significant, and achieving it requires a team that operates at the highest level. We seek individuals who embody our core traits: Scrappy, Curious, Optimistic, Persistent, and Empathetic . \n Your role We are hiring founding AI Systems Engineers to help build that machinery. \n This role is for engineers who like consequential junctions: between training outputs and deployable artifacts, between runtime systems and safe release, between quality claims and evidence, and between ambitious AI plans and systems that can actually carry them. \n This is not a research role, and it is not a generic support role. It is an implementation-heavy, building-focused engineering role on a small team responsible for making in-house AI capabilities easier to package, evaluate, deploy, promote, operate, and improve. \n Strong candidates may come from different technical backgrounds. Some will be strongest in productionization and platform systems. Some will lean toward runtime and serving. Some will lean toward evaluation and quality systems. What unifies them is not one toolchain or one narrow specialty. It is the ability to help move the same bottleneck: reducing the time and friction required to get in-house AI capabilities into reliable and scalable production, while preserving operational discipline and truthful quality judgment. \n AI Platform Engineering exists to shorten the path from emerging AI capability to reliable production impact. \n We build the shared systems, standards, and delivery pathways that let in-house models and AI capability packages move from candidate state into observable, rollback-safe production operation. Our work sits at the junction between model development, runtime systems, evaluation, and delivery. We enable the broader AI Platform division by making it faster and safer to ship new capabilities, improve existing ones, and learn from production behavior. \n This is a founding team. The systems, interfaces, and standards are still being shaped. The work is highly consequential, highly practical, and closely tied to the company’s broader AI strategy. We are not building one-off demos or isolated launches. We are building the machinery by which a growing AI organization can repeatedly deliver real capability into production. \n What you’ll do \n You will help design, build, and improve the systems that connect AI capability development to production reality. \n Depending on your strengths, that may include work such as: \n \n Improving how model and capability artifacts are packaged, versioned, promoted, and rolled back. \n Building or improving deployment and release pathways for AI-backed services. \n Enabling shadow-serving, staged rollout, and candidate-versus-incumbent comparison. \n Strengthening runtime behavior, observability, and debugging for model-backed systems. \n Building or automating evaluation systems that make release decisions evidence-based. \n Reducing bespoke coordination and strengthening the shared rails used by multiple AI teams. \n \n The exact balance will depend on your background and the team’s evolving needs. What will not vary is the mission: your work should make the broader AI Platform organization faster, safer, and more effective at turning in-house AI capability into production reality. \n Skills you’ll bring \n \n Bachelor's degree in Computer Science, Engineering, or equivalent related experience. \n 2 to 6 years of professional software engineering experience, with a proven track record of shipping production infrastructure or real systems that matter. \n Experience in writing solid, maintainable production code and applying strong software engineering fundamentals to solve complex debugging challenges. \n","salary_min":111000,"salary_max":133500,"location":"Kitchener, Canada","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["mlops","distributed-systems","agents","cloud"],"apply_url":"https://job-boards.greenhouse.io/dialpad/jobs/8512122002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-21T14:39:40Z","expires_at":"2026-06-29T14:19:36.048813Z","created_at":"2026-05-27T14:20:42.555713Z","updated_at":"2026-05-30T14:19:36.162681Z","company_name":"Dialpad","company_slug":"dialpad","company_logo_url":"https://www.google.com/s2/favicons?domain=dialpad.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/c5dcfa8b-bd45-4ef4-b917-e879853655e2"},{"id":"12749446-611e-48f3-b643-568d3c7be3f0","company_id":"053355fc-0162-4bb9-b414-cbf7679ee9c8","title":"Staff Product Manager - Infrastructure, Data \u0026 Security ","slug":"staff-product-manager-infrastructure-data-security-60ff4f34","description":"About Snorkel \n At Snorkel, we believe meaningful AI doesn’t start with the model, it starts with the data.\n We’re on a mission to help enterprises transform expert knowledge into specialized AI at scale. The AI landscape has gone through incredible changes between 2015, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!\n The Role: \n We are looking for a Technical Product Manager to own the roadmap and execution across Snorkel AI's Infrastructure organization, spanning three engineering pods: Core Services, Developer Experience, and Security. This is not a customer-facing product role, you will be the PM for internal platforms, data infrastructure, and security systems that the rest of engineering and the business depend on. You will be the single point of coordination across a broad surface area that currently lacks dedicated product leadership.\n What You Will Do: \n The Infrastructure org owns the data platform, event systems, observability, developer tooling, release pipelines, and the full security stack. The engineering leadership and pod leads are very technical but the org needs a PM who can translate business and compliance requirements into prioritized infrastructure investments, manage dependencies across pods and partner teams, and hold the line on scope and delivery timelines. Today, prioritization decisions are made ad hoc by engineering leads who are also deep in technical execution. This role gives the org a dedicated owner for roadmap clarity, stakeholder alignment, and cross-pod coordination.\n Business context and infrastructure strategy: \n \n Develop a deep understanding of Snorkel AI's business, how the datasets are sold, and how should the platform be deployed, and scaled to support customers across AI labs, enterprise and federal and use that context to work closely with engineering leaders and product PMs to translate business scaling, security and functional needs into deployment patterns, and go-to-market requirements into concrete, prioritized projects for the Infrastructure org. Without this, infrastructure roadmaps become technically interesting but disconnected from what actually moves the business forward.\n \n Roadmap and prioritization across all three pods: \n \n Work with engineering leads to define quarterly roadmaps for Core Services (data platform, metrics platform, event systems, observability, fraud detection, infrastructure cost management, platform customization infrastructure, SDS project infrastructure), Developer Experience (CI/CD, release pipelines, dev systems, AI dev tooling), and Security (Auth0 migration, AuthN/AuthZ, cloud security, encryption).\n Make trade-off decisions when pods compete for shared resources or when new asks land mid-quarter. The org carries significant surface area relative to headcount. Your job is to ensure the team is working on the highest-leverage problems, not just the loudest requests.\n \n Stakeholder management and requirements gathering: \n \n Be the interface between the Infrastructure org and its consumers: product engineering teams who depend on Dataset APIs, Platform APIs, and Packaging APIs; the data science and analytics teams who depend on Cascade and the transformation layer; GTM and customer success teams who surface compliance and security requirements from enterprise and federal customers.\n Translate customer compliance requirements (SOC 2, FedRAMP, GDPR) into concrete security and data governance work items with clear acceptance criteria.\n \n Adoption and internal evangelism:  \n \n Proactively engage product PMs and engineering leads to ensure teams are leveraging infrastructure services, SDKs, and shared libraries (unified data access, event bus, security SDKs, CI/CD tooling) rather than building one-off solutions.\n Track adoption metrics, SDK integration coverage, service onboarding rates, migration completions, and identify teams that are lagging or working around infrastructure offerings. Close the loop by either driving adoption or feeding unmet requirements back into the roadmap.\n The Infrastructure org's impact is only as real as its adoption. You own the push to make sure the work doesn't just ship, it lands.\n \n Cross-pod coordination on foundational initiatives: \n \n Drive initiatives that span multiple pods, such as the unified data access library (which touches Core Services for the library itself, Security for auth/RBAC/encryption enforcement, and Developer Experience for SDK distribution and testing tooling).\n Coordina","salary_min":240000,"salary_max":300000,"location":"Redwood City, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["llm","reinforcement-learning","generative-ai","mlops","infrastructure"],"apply_url":"https://job-boards.greenhouse.io/snorkelai/jobs/6001496004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-20T18:27:52Z","expires_at":"2026-06-29T14:03:06.795706Z","created_at":"2026-05-27T14:03:19.232673Z","updated_at":"2026-05-30T14:03:06.906572Z","company_name":"Snorkel AI","company_slug":"snorkel-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=snorkel.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/12749446-611e-48f3-b643-568d3c7be3f0"},{"id":"3261877c-be75-4d43-85d6-3e4911c5fc14","company_id":"5fac52d7-9b0b-4990-80a2-e2949dd0af1d","title":"Senior Software Engineer, ML/AI Platform","slug":"senior-software-engineer-mlai-platform-80ff4bf6","description":"Attentive® is the AI marketing platform for 1:1 personalization redefining the way brands and people connect. We’re the only marketing platform that combines powerful technology with human expertise to build authentic customer relationships. By unifying SMS, RCS, email, and push notifications, our AI-powered personalization engine delivers bespoke experiences that drive performance, revenue, and loyalty through real-time behavioral insights.\n  \n Recognized as the #1 provider in SMS Marketing by G2, Attentive partners with more than 8,000 customers across 70+ industries. Leading global brands like Crate and Barrel, Urban Outfitters, and Carter’s work with us to enable billions of interactions that power tens of billions in revenue for our customers.\n  \n With a distributed global workforce and employee hubs in New York City, San Francisco, London, and Sydney, Attentive’s team has been consistently recognized for its performance and culture. We’re proud to be included in  Deloitte’s Fast 500  (four years running!),  LinkedIn’s Top Startups ,  Forbes’ Cloud 100 (five years running!),  Inc.’s Best Workplaces , and the  Human Rights Campaign Foundation's Corporate Equality Index !\n About the Role We’re looking for a self-motivated, highly driven Senior Software Engineer to join our Machine Learning Platform  (MLPlatform) team. As a team, we enable Attentive’s Machine Learning (ML) practice to directly impact Attentive’s AI product suite through the tools to train, serve, and deploy ML models with higher velocity and performance, while maintaining reliability. We build and maintain a foundational ML platform that spans the full ML lifecycle for use by ML engineers and data scientists. This is an exciting opportunity to join a rapidly growing ML Platform team at the ground floor, with the ability to drive and influence the architectural roadmap, enabling the entire ML organization at Attentive. This team and role are responsible for building and operating the ML data, tooling, serving, and inference layers of the ML platform. We are excited to bring on more engineers to continue expanding this stack.\n What You’ll Accomplish \n \n Unlock offline \u0026 real-time access to trillions of data points for our ML and Data Science teams.\n Manage, expand, and optimize our feature store that enables feature engineering, multi-TB scale training jobs, and offline / real-time inferencing.\n Support PB scale data operations on the feature store using Apache Spark, Spark Structured Streaming, Kafka, and Ray.\n Partner with other teams and business stakeholders to deliver ML and AI initiatives.\n \n Your Expertise \n \n You have been working in the areas of Data Engineering / MLOps for 5+ years, and have built and matured the pipelines of a PB-scale feature store.\n You have deep Apache Spark, Spark Streaming, and Ray Data experience and built data pipelines for ML use cases using these tools.\n You understand the correlation between data cardinality, query plans, configuration settings, and hardware and the impact of each on data pipeline performance .\n You know/have created infrastructure for Training ML models/fine-tuning LLMs. \n You understand the key differences between online and offline ML inferences and can voice the critical elements to be successful with each to meet business needs.\n \n What We Use \n \n Our infrastructure runs primarily in Kubernetes hosted in AWS’s EKS.\n Infrastructure tooling includes Istio, Datadog, Terraform, CloudFlare, and Helm.\n Our backend is Java / Spring Boot microservices, built with Gradle, coupled with things like DynamoDB, Kinesis, AirFlow, Postgres, Planetscale, and Redis, hosted via AWS.\n Our frontend is built with React and TypeScript, and uses best practices like GraphQL, Storybook, Radix UI, Vite, esbuild, and Playwright.\n Our automation is driven by custom and open source machine learning models, lots of data and built with Python, Metaflow, HuggingFace 🤗, PyTorch, TensorFlow, and Pandas.\n \n You'll get competitive  perks and benefits , from health \u0026 wellness to equity, to help you bring your best self to work.\n For US based applicants: \n \n The US base salary range for this full-time position is $180,000 - $250,000 annually   + equity + benefits\n Our salary ranges are determined by role, level and location\n \n #LI-EZ1   \n By applying for this position, your data will be processed as per Attentive's Privacy Policy . \n Attentive Company Values \n Default to Action - Move swiftly and with purpose\n Be One Unstoppable Team - Rally as each other’s champions\n Champion the Customer - Our success is defined by our customers' success\n Act Like an Owner  - Take responsibility for Attentive’s success\n  \n Learn more about AWAKE , Attentive’s collective of employee resource groups.\n  \n If you do not meet all the requirements listed here, we still encourage you to apply! No job description is perfect, and we may also have another opportunity that closely matches you","salary_min":180000,"salary_max":250000,"location":"United States","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["api-design","data-pipeline","pytorch","fine-tuning","tensorflow","microservices","llm","mlops"],"apply_url":"https://job-boards.greenhouse.io/attentive/jobs/4252255009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-19T20:47:29Z","expires_at":"2026-06-29T14:18:28.156485Z","created_at":"2026-05-27T14:19:20.116375Z","updated_at":"2026-05-30T14:18:28.272305Z","company_name":"Attentive","company_slug":"attentive","company_logo_url":"https://www.google.com/s2/favicons?domain=attentive.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/3261877c-be75-4d43-85d6-3e4911c5fc14"}],"page":1,"per_page":20,"total":614,"total_pages":31}