{"access":{"catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. 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Our customers include the largest companies in telecom, healthcare, insurance, manufacturing, consumer goods, and global social organizations.\n\nWe research and deploy technologies that power AI-native operations — both for our partners and for Distyl itself. Our work spans research into self-constructing systems, the development of the most reliable execution of AI systems, and products that transform mission-critical workflows. As a result, Distyl's technologies affect some of the world's largest operations — from hundreds of millions of consumer interactions to tens of millions of supply chain transactions and millions of patient journeys.\n\nDistyl is backed by leading investors including Lightspeed Venture Partners, Khosla Ventures, Coatue, DST Global, and the board-members of 20+ F500s. \n\n\n\n\nWHAT WE ARE LOOKING FOR\n\nWe're hiring a Infrastructure Engineer to own how Distyl engineers build, test, secure, and release software. Our independently versioned services span multiple repositories and deploy into customer environments across AWS, Azure, and GCP, with different security, connectivity, and upgrade constraints.\n\n\n\nThis is a hands-on role with scope matched to your experience, from owning complex delivery systems to setting technical direction across teams. You'll build shared delivery systems and coding-agent workflows that make releases reliable, failures actionable, and engineers more productive. Your systems will shape how every Distyl engineer ships.\n\n\n\n\nKEY RESPONSIBILITIES\n\n - Technical ownership. Set direction and priorities for build, test, release, and coding-agent infrastructure. Choose tools and standards with engineering partners, and own implementation, adoption, and operation across teams\n\n - Build systems and repository standards. Standardize repository conventions and reusable build/test workflows. Make builds reproducible and efficient through explicit dependencies, consistent tooling, and effective caching\n\n - Release engineering. Build once and promote versioned bundles of immutable artifacts through release stages, with source traceability and test and security gates. Partner with Platform, Runtime, and Customer Delivery on integration and compatibility testing, configuration and schema changes, and recovery. Support customer-registry distribution and offline packaging where required\n\n - Agent-enabled engineering. Equip engineers with reusable coding-agent tools to diagnose failures, propose fixes, and handle routine work. Include tests, scoped permissions, failure handling, and independent output validation; measure quality and time saved\n\n - Software supply-chain security. Implement artifact signing and provenance, software bills of materials (SBOMs), isolated build runners, scoped CI credentials, and vulnerability gates. Integrate dependency and container remediation into engineering workflows\n\n - Developer experience. Understand engineers' workflows and remove friction with self-service tools, documentation, and diagnostics that distinguish code failures from tooling issues. Use developer feedback to guide incremental migrations, earn adoption, and reduce manual work\n\n - Engineering productivity and alerting. Track build/test latency, pipeline reliability, flaky-test rates, release lead time, and tooling adoption. Use these signals and developer feedback to reduce flaky tests and bottlenecks. Set service-level objectives and route actionable alerts to owners\n   \n   \n\n\nWHO YOU ARE\n\n - 10+ years of experience in software engineering, developer productivity, SRE, or related infrastructure engineering roles\n\n - Experience enabling engineers to use coding agents through reusable tools, workflows, and guidance, with independent validation and scoped access\n\n - Experience owning production build, test, and release infrastructure across services and teams, including versioned release bundles and compatibility testing\n\n - Hands-on experience with build systems, dependency management, reproducibility, performance diagnosis, and caching\n\n - Strong Python skills for building and debugging backend services and maintainable automation. Comfortable navigating unfamiliar code, including JavaScript frontends\n\n - Strong Linux fundamentals and experience building and maintaining secure container images. Working knowledge of Kubernetes, GitOps, infrastructure as code, and cloud deployment\n\n - Hands-on experience implementing software supply-chain controls. Demonstrated security judgment across permissions, credentials, dependencies, artifacts, and remediation\n\n - Experience building developer platforms, automated remediation, or reusable agent tooling adopted by other engineers\n\n - A record of improving build/test performance, release reliability, and tooling adoption using metrics and developer feedback\n\n - Experience","salary_min":200000,"salary_max":250000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["microservices","agents","healthcare","infrastructure"],"apply_url":"https://jobs.ashbyhq.com/distyl/6b13df85-e705-4a69-9340-0e83c54e78c5/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T23:19:53.802Z","expires_at":"2026-11-13T17:03:04.859555Z","created_at":"2026-10-14T15:25:08.324287Z","updated_at":"2026-10-14T17:03:04.863136Z","company_name":"Distyl AI","company_slug":"distyl-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=distyl.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ebabc8b3-5891-442b-a4d0-7a3ac1388c2d"},{"id":"6aceb6c9-aa17-4cd3-81ca-d26284538246","company_id":"5b08d89d-9846-4ef7-8b53-d07ef3ee6c19","title":"Software Engineer, ML Infrastructure","slug":"software-engineer-ml-infrastructure-f2fd5868","description":"About Arc Institute \n Arc Institute is an independent nonprofit research organization at the interface of artificial intelligence and biology, working to accelerate scientific progress and understand the root causes of complex diseases. Founded in 2021 and based in Palo Alto, Arc partners with Stanford University, UC Berkeley, and UC San Francisco.\n Unlike academia, our scientists have long-term funding and industry-like resources. Unlike industry, they're free to pursue high-risk, long-term research without commercial pressures. Arc's Technology Centers and Core Investigator labs work side by side, integrating experimental and computational biology under one roof to tackle problems neither could solve alone.\n Our two Institute Initiatives reflect this model in action:\n \n Virtual Cell Initiative : Building a full-stack virtual cell model to identify disease mechanisms and nominate drug targets,  accelerating the path from biological insight to clinical trials.\n Alzheimer's Disease Initiative : Mapping the genes, pathways, and environmental factors behind Alzheimer's disease to develop drug candidates that address root causes.\n \n More than 300 Arconauts work together at our Palo Alto headquarters, backed by substantial long-term philanthropic funding.\n About the position \n You'll join Arc's Infrastructure Engineering team and own the data and evaluation of ML infrastructure for Arc's virtual cell models. You'll start with two systems: our Model Performance Dashboard, which benchmarks those models, and the pipeline that turns single-cell data into their training sets. You'll set the technical direction for both and connect them, so every new dataset version is judged by what it does to the models. Later in your first year, the role widens into the engineering that makes the ML team's training and evaluation run fast and at scale, lab-in-the-loop infrastructure, validation frameworks for agent-written software, and Arc's rules for what research data we bring in, keep and share. The role consolidates ownership that is split across several teams today. \n Why this position could be the best job you’ve ever had \n \n You will contribute to building a virtual cell. Your evals show which data makes Arc's models better, and the answer shapes which experiments Arc's labs run next.\n You will own the platform end to end, and you work directly with the ML researchers and scientists who rely on it.\n \n About you \n \n You finish what you own, and you fix what's broken without being asked.\n You're curious about the biology, enough to learn single-cell perturbation data and argue about what an eval should measure. \n You're steady when priorities shift. Research is high-risk, so you test ideas early and change course when the evidence says to.\n You don't wait on scarce expert time. If proving a dataset's value means running the model evals yourself, you run them.\n \n In this position you will \n \n Own how Arc measures the performance of its virtual cell models, starting with the backend of our Model Performance Dashboard, which you'll take over within your first two months. \n Decide with the ML team which evaluation tools they need, then design and build them so researchers can evaluate and compare models themselves, and measure what each dataset version does to model performance.\n Redesign the pipeline that turns single-cell data into training sets for our virtual cell models, then run it with AI agents: agents find, process and curate datasets, and automated quality checks, not manual review, decide whether a dataset version is ready for model training.\n Own the lab-in-the-loop infrastructure that turns model performance results into the case for what Arc's labs should test next, working directly with the science leads who design those experiments . \n Build or buy validation frameworks (tests, evals, QC gates, policies) that let agent-written code and apps ship without line-by-line human review. Start with our team, where you'll also own how we work with coding agents, then extend them to apps that scientists across Arc build themselves.\n Own the engineering that makes the ML team's training and evaluation run fast, reliably and at scale. The ML team owns the research and the models. Once a model is ready to push, it enters the train-and-eval CI system you own, which feeds our Model Performance Dashboard. You'll also make multi-node training a supported path, track down distributed-training failures, and measure and raise throughput and GPU utilization (MFU) on clusters that Arc IT operates.\n \n Requirements \n \n Bachelor’s degree with 8 years of relevant experience, or Master’s degree with 6 years of relevant experience.\n You've owned a production data or ML platform that researchers depend on, end to end (pipelines, storage, versioning), and set its technical direction.\n You design and build systems with AI tools and agents, and you make their output verifiable with evals, QC gates or tests before anyone relies on it.\n ","salary_min":191000,"salary_max":237000,"location":"Palo Alto, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","healthcare","pytorch","distributed-systems","agents","machine-learning","infrastructure"],"apply_url":"https://job-boards.greenhouse.io/arcinstitute/jobs/6222681004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-12T22:16:26Z","expires_at":"2026-11-13T17:03:53.841883Z","created_at":"2026-10-14T15:25:57.647484Z","updated_at":"2026-10-14T17:03:53.845803Z","company_name":"Arc Institute","company_slug":"arc-institute","company_logo_url":"https://www.google.com/s2/favicons?domain=arcinstitute.org\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/6aceb6c9-aa17-4cd3-81ca-d26284538246"},{"id":"0e915c96-e091-4d28-9652-01f3efa478ae","company_id":"f5ee7284-a657-4da2-b351-cb806a3681cd","title":"Infrastructure Security Engineer (Bare Metal \u0026 Platform)","slug":"infrastructure-security-engineer-bare-metal-platform-65c20bb1","description":"SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars. SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next.\n ABOUT THE ROLE: \n We are seeking a hands-on Infrastructure Security Engineer to build and secure SpaceXSI's infrastructure from the bare-metal layer up through the platform and application stack. In this role, you will design, implement, and harden physical and private-cloud foundations—servers, networks, identity, and orchestration—and carry that security posture into containers, automation, and hybrid connectivity with public cloud.\n This role focuses on securing critical infrastructure spanning data centers, colo, and hybrid environments, with work that may span platforms such as SpaceXSI, X Social, X Money, and related systems. You’ll operate with an automation-first mindset: building secure foundations at the metal and OS layer, hardening platforms at scale, and partnering closely with engineering so security is embedded in how we build and run systems—not bolted on after the fact.\n RESPONSIBILITIES: \n Bare metal, data center \u0026 network security\n \n Design and implement secure bare-metal and private-cloud architectures (servers, storage, out-of-band management, BIOS/UEFI/BMC firmware posture)\n Harden physical and virtual fleets: OS baselines (Linux/Windows), CIS benchmarks, patch and vulnerability management, and secure boot / measured boot where applicable\n Hands-on firmware, BMC/iDRAC/iLO, PXE/Kickstart provisioning, and hardware lifecycle security\n Build and secure network foundations: segmentation, load balancers, DNS, PKI, NAC, and IDS/IPS on real hardware\n Own hybrid connectivity between on-premises and cloud (site-to-site VPN, Direct Connect / Interconnect / ExpressRoute, SD-WAN, zero-trust access)\n Manage identities and privileged access across Active Directory / LDAP / Kerberos and cloud IAM (federation, SAML/OIDC, PAM)\n Assist with security assessments and audits of physical, on-premises, and hybrid infrastructure\n Monitor and remediate infrastructure to comply with regulations and best practices (e.g., PCI, NIST CSF, GDPR, HIPAA)\n \n Platform, containers \u0026 developer enablement\n \n Design and implement secure container and Kubernetes standards on bare metal and private cloud (not only managed EKS/GKE/AKS), including RBAC, network policy, and secrets\n Develop and maintain Infrastructure as Code and configuration management—especially Puppet—with embedded security controls for physical and virtual fleets\n Collaborate with development teams so security best practices are integrated into CI/CD pipelines\n Secure and enhance CI/CD pipelines; integrate and maintain code and image scanning platforms\n \n Detection, operations \u0026 tooling\n \n Monitor and respond to security events and incidents spanning bare metal, network devices, hosts, and hybrid cloud\n Maintain SIEM data pipelines that ingest on-premises network, host, and cloud telemetry for reliable alerting\n Build, deploy, and maintain security operations infrastructure using Python, Terraform, and Puppet\n Develop dashboards and alerts from security metrics across physical and platform layers\n Develop and maintain infrastructure security policies, standards, and procedures from metal through platform\n Own security projects end to end: identify issues and implement solutions\n Stay current with emerging threats and mitigations for bare-metal, firmware, network, and hybrid-cloud infrastructure\n \n BASIC QUALIFICATIONS: \n \n Bachelor's degree in Computer Science, Cybersecurity, or a related field\n 3–5 years of experience in infrastructure security, platform security, or related hands-on roles\n Proven experience securing bare-metal, data-center, or private-cloud environments (not cloud-only resumes)\n Hands-on experience with firmware, BMC/iDRAC/iLO, PXE/Kickstart provisioning, and hardware lifecycle security\n Strong proficiency with Puppet for configuration management and fleet hardening in production\n Strong understanding of network security concepts and protocols, including hands-on work with firewalls, segmentation, and hybrid connectivity\n Experience with hybrid identity (AD/LDAP or similar) integrated or federated with cloud IAM\n Experience with Infrastructure as Code (e.g., Terraform) applied to physical or VM fleets\n Familiarity with containerization ","salary_min":100000,"salary_max":258000,"location":"Palo Alto, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["payments","cloud","data-pipeline","security","infrastructure"],"apply_url":"https://job-boards.greenhouse.io/xai/jobs/5262880007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-10T01:45:54Z","expires_at":"2026-11-13T16:55:31.592515Z","created_at":"2026-10-14T15:19:21.532282Z","updated_at":"2026-10-14T16:55:31.595612Z","company_name":"xAI","company_slug":"xai","company_logo_url":"https://www.google.com/s2/favicons?domain=x.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/0e915c96-e091-4d28-9652-01f3efa478ae"},{"id":"3b73d4e3-8873-4465-bf7b-ec795560a17b","company_id":"7455e78c-482b-4f7b-9d62-11b78645818a","title":"Software Engineer 2 - Development Infrastructure ","slug":"software-engineer-2-development-infrastructure-3dfd6ec0","description":"About the Role\n Abnormal AI empowers businesses of all sizes to combat cybercrime through our innovative cloud products. To achieve our mission, we're building a suite of interconnected security solutions on a unified platform, designed to operate flawlessly in global data centers.\n The Development Infrastructure team plays a critical role in multiplying the impact of our product teams. We create an environment that fosters high-quality software development, and engineering excellence. This team has become instrumental in the success of our engineering organization.\n You are someone who:\n \n Thrives in a collaborative, fast-paced environment\n Possesses a passion for helping others build world-class systems\n Demonstrates exceptional teamwork skills\n Embraces continuous learning and enjoys tackling complex problems\n \n What you will do \n \n Lead the design and delivery of complex, multi-component features and systems of the Development Infrastructure team's offerings, and shape the roadmap for this area.\n Develop and maintain CI/CD infrastructure and tools for Engineering to develop and ship their changes reliably with less effort.\n Design and implement enhancements to GHA infrastructure, base Docker images and build/deploy pipelines with improvements in scalability, performance and costs.\n Use AI tools to explore and compare solutions, and adapt the generated changes to real production constraints, weighing system complexity and long-term maintainability.\n Take ownership of on-call issues, including complex issues where the root cause is unclear, and fix the systemic gaps behind them.\n Participate in planning, code reviews, standups, and other aspects of the software development life cycle, and coach other engineers on the team.\n \n Must Haves\n \n 3+ years of professional experience as a hands-on engineer building backend systems and/or platforms.\n 2+ years of experience working with Python.\n Experience delivering complex projects end-to-end in a cloud native environment.\n Experience building or operating CI/CD, build, or deployment systems in production, including on-call and incident response.\n Understanding of distributed systems, containerization and microservices.\n Practical experience using Generative AI tools in day-to-day engineering workflows.\n \n Nice to Haves\n \n Experience creating and maintaining CI/CD systems.\n Experience with GitHub Actions.\n Experience with Go.\n Experience with Bazel.\n Experience working on or with teams like “Engineering Effectiveness”, “Engineering Productivity”, “Development Experience”, “Development Infrastructure” or similar.\n Knowledge of Docker, Kubernetes, or similar technologies.\n Familiarity with Generative AI tools such as Codex and Claude, and experience driving AI-first workflows and solutions.\n \n  \n #LI-ML1\n Actual compensation will be determined based on several non-discriminatory factors including skills, experience, qualifications, and geographic location. In addition to base salary, this role may be eligible for bonus or incentive compensation, equity, and a comprehensive benefits package.\n Base salary range:\n $149,200 — $214,500 USD \n A note on AI in our process:  Abnormal AI uses AI-assisted tools to help our recruiting team prepare for candidate interviews. These tools analyze resume content and role requirements to suggest interview questions and areas for the interviewer to explore.They do not make hiring decisions or screen candidates automatically. Every decision about a candidacy is made by a person. Further, if your application is successful and Abnormal AI makes a conditional offer of employment, we will carry out pre-employment checks which must be successfully completed to progress to a final offer. All processes and pre-employment checks are in line with prevailing legislation and Abnormal AI's policies relevant to our security and privacy standards.\n Abnormal AI is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status or other characteristics protected by law. For our EEO policy statement please click here . If you would like more information on your EEO rights under the law, please  click here .","salary_min":149200,"salary_max":214500,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"mid","tags":["generative-ai","microservices","distributed-systems","infrastructure"],"apply_url":"https://abnormal.ai/careers/jobs/8015189003?gh_jid=8015189003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-09T23:40:56Z","expires_at":"2026-11-13T16:55:40.593701Z","created_at":"2026-10-14T15:19:46.257997Z","updated_at":"2026-10-14T16:55:40.597796Z","company_name":"Abnormal Security","company_slug":"abnormal-security","company_logo_url":"https://www.google.com/s2/favicons?domain=abnormalsecurity.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/3b73d4e3-8873-4465-bf7b-ec795560a17b"},{"id":"a5933db7-95b3-4868-a949-7067b206453d","company_id":"332b7698-676b-4a3e-8b02-81b1195c5af6","title":"Sr. Automation Engineer - IT Asset Management","slug":"sr-automation-engineer-it-asset-management-9e6ebd4d","description":"Sr. Automation Engineer - IT Asset Management \n Req ID: GAQ427R222 \n About the Role \n As a Senior Automation Engineer on the IT Asset Management team at Databricks, you will be the technical engine behind transforming how we manage ~$XXM/yr in asset spend across SaaS licenses and hardware. Your primary mission is to eliminate manual toil and build scalable, AI-powered automation that makes asset operations fast, accurate, and effortless as Databricks scales past 14,000 employees.\n Your initial focus will be on IT asset management workflows: automating new hire laptop provisioning, SaaS license lifecycle management, asset data reconciliation, and hardware recovery. Over time, you will expand your automation footprint across IT Support and Operations, including IT support workflows, AV/VC and event support, and incident response management.\n This role requires a strong automation-first mindset, the ability to learn new domains quickly, and comfort building with AI/LLM tools. You will report to the Senior Manager of IT Asset Management.\n The Impact You Will Have \n \n New hire laptop provisioning: Build end-to-end automation for assigning, configuring, and shipping laptops to new hires, targeting a 75% reduction in manual ticket toil.\n SaaS license lifecycle: Automate license consumption monitoring, inactivity-based seat clawbacks, renewal alerting, and cost optimization workflows to help drive $1.5M+ in annual savings and cost avoidance.\n Asset data quality: Automate CMDB/inventory reconciliation, deduplication, and multi-device detection to bring asset records to audit-ready accuracy.\n Offboarding asset recovery: Automate tracking and recovery of hardware from exiting employees, reducing loss and improving chain-of-custody compliance.\n Broader IT Ops automation (over time): Extend automation into IT support, AV/VC and event support, and incident response management as the ITAM automation foundation matures.\n \n What We Are Looking For \n Core Qualifications \n \n 5+ years of experience in IT systems engineering, automation engineering, or a related technical role.\n Proven track record of delivering complex, multi-system automation projects with measurable business impact.\n Strong automation-first mindset: you see a manual process and immediately think about how to build a scalable, reliable automated solution.\n Ability to operate with minimal guidance, take ownership of ambiguous problems, and deliver consistently high-quality work in a fast-paced environment.\n We value a great generalist who can learn a domain quickly over a narrow specialist. You should be comfortable ramping into unfamiliar systems and business processes and building effective automation without needing deep prior domain expertise.\n \n AI and LLM Automation \n \n Hands-on experience building workflows, agents, or tools powered by large language models (e.g., Claude, GPT, Gemini, or similar).\n Ability to design AI-powered automation that handles unstructured inputs, makes decisions, and integrates with enterprise systems.\n Experience with prompt engineering, agent frameworks, or AI-assisted code generation in a production or operational context.\n Comfort evaluating and selecting the right AI approach for a given problem, whether that is a deterministic rule, an LLM call, or a hybrid.\n \n Automation and Integration \n \n Experience building and troubleshooting integrations between IT platforms such as ITSM tools (FreshService, ServiceNow, or similar), identity platforms, and asset management systems.\n Experience designing automation for IT operational processes: onboarding/offboarding, access provisioning, license management, asset tracking, or similar.\n Familiarity with REST APIs, scripting (Python, Bash, or similar), and the ability to connect systems that were not designed to talk to each other.\n Experience with data pipelines, ETL, or data quality workflows is a plus.\n \n Collaboration and Communication \n \n Strong cross-functional collaboration skills. Comfortable working with IT operations, security, procurement, finance, and vendor partners.\n Effective at documenting solutions and training peers on new tools and processes.\n Clear communicator who can translate complex technical work into business terms for non-technical stakeholders.\n \n Nice to Have \n \n Experience with Databricks, data lakehouse architectures, or SQL-based analytics platforms.\n Experience with IT asset management or SaaS management tools (e.g., Oomnitza, Productiv, Zluri, or similar).\n Experience with no-code/low-code automation platforms (e.g., Tines, Workato, or similar).\n Familiarity with ITIL processes or IT service management frameworks.\n \n Location \n US-based, West Coast preferred.\n  \n Pay Range Transparency \n Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles.  Actual compensation packages ar","salary_min":121800,"salary_max":167550,"location":"Bellevue, WA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","code-generation","data-pipeline","llm","api-design","infrastructure"],"apply_url":"https://databricks.com/company/careers/open-positions/job?gh_jid=8829047002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-09T18:09:43Z","expires_at":"2026-11-13T16:55:16.60437Z","created_at":"2026-10-14T15:19:06.003672Z","updated_at":"2026-10-14T16:55:16.607166Z","company_name":"Databricks","company_slug":"databricks","company_logo_url":"https://www.google.com/s2/favicons?domain=databricks.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a5933db7-95b3-4868-a949-7067b206453d"},{"id":"8f18ce3b-de45-44c2-bcb4-bc1c243cc19a","company_id":"e3915539-5a8f-4461-9f26-06366a918674","title":"Senior AI Infrastructure Engineer, Physical Infrastructure","slug":"senior-ai-infrastructure-engineer-physical-infrastructure-ca6b4797","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 CorpTech Infrastructure Engineering builds and operates the foundational infrastructure that powers Anduril at large. We give engineers, researchers, and product teams across the company a place to deploy fast, scalable infrastructure without having to become infrastructure experts themselves. As Anduril's AI and autonomy ambitions grow, our team is responsible for delivering the next generation of compute, networking, and storage capabilities that make cutting edge model training and inference possible company wide.\n ABOUT THE JOB\n We’re looking for a Senior AI Infrastructure Engineer to lead the vision, execution, and long-term stability of how Anduril trains with GPUs at scale. In this role, you will take absolute ownership of cluster robustness, ensuring our high-performance GPU systems are highly available, fault-tolerant, and resilient for ML platform and research teams company-wide. This is a highly hands-on role where your primary focus is logical stability and automated resilience—building self-healing mechanisms to proactively detect and isolate hardware faults, tuning NCCL and high-speed networking, and optimizing Kubernetes, Run:AI, and Ray scheduling. By replacing manual triage with automated deployment tooling and deep observability, you will ensure our massive-scale training infrastructure runs seamlessly and scales without linear headcount growth.\n WHAT YOU'LL DO\n \n Rack, stack, cable, and bring up GPU compute (H200/B200/B300, NVL72) including physical topology, power, cooling, firmware/BIOS, and burn in validation.\n Build and tune the interconnect fabric (NVLink, InfiniBand, RoCE, Spectrum-X) connecting hundreds of GPUs into low latency training and inference clusters.\n Integrate high performance parallel storage (VAST, DDN, Weka) to sustain the throughput demanded by distributed training and terabyte scale multi modal datasets across Anduril's programs.\n Automate cluster deployment and configuration end to end, including infrastructure as code for bring up, firmware/driver management, and fabric config, so new capacity comes online with minimal manual work.\n Operate and extend our Kubernetes/Run:AI environment for GPU scheduling, quota management, and multi tenant workload isolation across research and engineering teams company wide.\n Own fleet health: monitoring, alerting, and rapid triage of hardware and network faults (bad transceivers, GPU Xid errors, NCCL/collective failures, RoCE congestion).\n Onboard engineers and researchers onto the platform and act as their escalation point, working directly alongside them to debug, train, and optimize their workloads whenever infrastructure, not the model, is the bottleneck.\n Partner with product facing teams across Anduril to understand emerging compute needs and translate them into platform capability.\n \n REQUIRED QUALIFICATIONS\n \n 10+ years in a hands on infrastructure, HPC, or datacenter engineering role supporting GPU compute at scale.\n Hands on experience with H200/B200/B300 (or comparable) GPU systems: bring up, cabling, firmware/driver management.\n Experience with high performance interconnects (NVLink, InfiniBand, RoCE, Spectrum-X) in clusters of hundreds of GPUs.\n Experience with high performance parallel storage (VAST, DDN, Weka, Lustre, or similar).\n Kubernetes required; Run:ai or similar GPU scheduling/orchestration experience strongly preferred.\n Strong automation background. You build repeatable, automated deployment pipelines rather than manual processes.\n Able to lift/move 50+ lbs and perform physical datacenter work (rack/stack/cable/troubleshoot).\n Eligible to obtain and maintain an active U.S. Top Secret clearance.\n \n PREFERRED QUALIFICATIONS\n \n Experience with NVIDIA NVL72 rack scale systems.\n Experience supporting LLM token serving/inference infrastructure alongside training clusters.\n Network fabric tuning experience (congestion control, adaptive routing, QoS) for RoCE/InfiniBand at scale.\n Familiarity with GPU/network observability tooling (DCGM, fabric telemetry) and automated fault detection.\n Experience supporting infrastructure as a shared platform serving multiple internal customer teams with differing requirements.\n US Salary Range\n $166,000 — $220,000 USD \n The salary range for ","salary_min":166000,"salary_max":220000,"location":"Seattle, WA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["distributed-systems","payments","cloud","llm","computer-vision","infrastructure"],"apply_url":"https://boards.greenhouse.io/andurilindustries/jobs/5261398007?gh_jid=5261398007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-08T17:17:31Z","expires_at":"2026-11-13T16:57:16.163037Z","created_at":"2026-10-14T15:21:01.333522Z","updated_at":"2026-10-14T16:57:16.165847Z","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/8f18ce3b-de45-44c2-bcb4-bc1c243cc19a"},{"id":"89d58d57-03e8-4684-83b2-3121d619b65f","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Software Engineer, ML Serving Platform","slug":"software-engineer-ml-serving-platform-c430ad40","description":"About the Team \n DoorDash’s ML Serving Platform delivers tens of millions of predictions per second , powering search, recommendations, advertising, delivery estimates, and logistics across DoorDash, Wolt, and Deliveroo.\n Our customers are modelers and engineering teams across our internal business verticals. We build self-serve infrastructure that empowers them to bring new models into production, adopt open source software and models, and expand what they can accomplish with machine learning at scale.\n We work at the intersection of a rapidly evolving open source ecosystem and demanding production workloads. With active customer demand and growing modeling ambitions, we’re advancing the infrastructure behind today’s predictions while building the capabilities that enable the next generation of ML innovation.\n About the Role \n You’ll help build the next generation of our ML serving platform, connecting request routing and online feature retrieval with model inference on CPU and GPU infrastructure. You’ll tackle challenging infrastructure problems involving latency, reliability, resource efficiency, and scale, and make those capabilities accessible through self-serve tools and workflows.\n Working alongside experienced platform engineers, you’ll own defined projects from technical design and implementation through testing, rollout, and production support. You’ll partner directly with internal teams to understand emerging modeling requirements, remove adoption barriers, and turn advances in open source technology into measurable production improvements.\n You’re excited about this opportunity because you will… \n \n Empower modelers through self-serve infrastructure. Build tools, APIs, and workflows that help internal teams deploy, configure, validate, and operate models independently, accelerating ML adoption across business verticals.\n Bring open source innovation into production. Evaluate and integrate evolving inference frameworks and enable open source models, translating promising capabilities into reliable, efficient services at scale.\n Solve demanding inference infrastructure problems. Improve the systems that route prediction requests, retrieve online features, and execute models on a platform serving tens of millions of predictions per second.\n Help evolve our disaggregated serving architecture. Build modular components that allow routing, feature retrieval, and model execution to evolve and scale independently as workloads and modeling requirements change.\n Improve Kubernetes deployment and autoscaling. Help workloads respond to changing traffic while meeting latency and availability requirements and using CPU and GPU resources efficiently.\n Make adoption and rollout easier. Build integrations, validation, and migration tooling that help teams adopt new serving capabilities and our unified global platform with confidence.\n Own performance and reliability in production. Use benchmarking, profiling, metrics, and tracing to identify bottlenecks; participate in on-call and improve automation and runbooks to make the platform easier to operate.\n \n We’re excited about you because… \n \n You have 2+ years of software engineering experience building and maintaining production services or infrastructure.\n You have strong computer science fundamentals and proficiency in a backend or systems programming language such as Java, Kotlin, Go, C++, or Python.\n You understand distributed systems fundamentals, including concurrency, networking, timeouts, failure handling, and performance trade-offs.\n You enjoy challenging infrastructure problems and can independently turn a defined problem into a technical design, tested implementation, and safe production rollout.\n You have experience debugging production systems and using operational data to improve reliability, performance, or cost.\n You care about the engineers using your platform and collaborate effectively with customers and partners to make complex infrastructure easier to adopt.\n You hold a degree in Computer Science or a related field, or have equivalent practical experience.\n \n Nice to Have \n \n Experience with ML inference infrastructure, online feature retrieval, or other latency-sensitive distributed services.\n Experience operating containerized workloads on Kubernetes, including deployment, resource management, or autoscaling.\n Experience building self-serve developer platforms, APIs, or automation that helps other engineers move faster.\n Experience integrating open source infrastructure or models and validating their behavior under production workloads.\n Familiarity with CPU/GPU performance profiling, inference runtimes, or model deployment workflows.\n \n Applications for this position are accepted on an ongoing basis\n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work lo","salary_min":130600,"salary_max":192000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["mlops","distributed-systems","fine-tuning","cloud","gpu","healthcare","infrastructure"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8263726","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-08T14:17:36Z","expires_at":"2026-11-13T17:03:28.772861Z","created_at":"2026-10-14T15:25:31.414102Z","updated_at":"2026-10-14T17:03:28.775658Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/89d58d57-03e8-4684-83b2-3121d619b65f"},{"id":"84267438-bcb6-4a44-97ac-4f18bdb2ad77","company_id":"a0000000-0000-0000-0000-000000000001","title":"Staff+ Software Engineer, Research Systems Engineering","slug":"staff-software-engineer-research-systems-engineering-27aecffe","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the role\n Anthropic's Infrastructure organization builds and operates the distributed systems that train, serve, and secure our AI models. Every other team at Anthropic depends on these systems.\n In this role, you'll work directly with our Research teams to build the reliable, scalable, performant infrastructure our research depends on. This is a cross-functional systems team: you'll scope and lead complex, multi-month infrastructure projects and resolve the performance and scalability bottlenecks that limit how fast we can grow.\n Key responsibilities\n \n Independently scope and lead complex, multi-month infrastructure projects, from an ambiguous starting point through to a production system\n Build deep partnerships with researchers and Research teams to understand their needs and deliver for them\n Mentor other engineers and help raise the technical bar for the team\n Build alignment on technical direction across multiple teams, working through ambiguous problem spaces\n Take ownership of the reliability, scalability, and security of the systems you build as usage and complexity grow\n Lead the improvement of operational processes across Infrastructure, such as incident response, postmortems, and on-call rotations, so the team learns from every incident\n \n Minimum qualifications\n \n Experience designing, building, and operating large-scale distributed systems or infrastructure in production\n Experience independently scoping and delivering complex, ambiguous, multi-month technical projects\n Prior experience as a technical lead or mentor for other engineers\n Experience making architectural decisions that other engineers and teams build on top of\n Strong software engineering fundamentals and proficiency in at least one programming language (e.g., Python, Rust, Go, Java)\n Experience with modern cloud infrastructure (e.g., Kubernetes, infrastructure as code, AWS, GCP)\n Strong written and verbal communication skills, with experience building alignment across multiple teams or stakeholders\n \n Preferred qualifications\n \n 10+ years of software engineering experience, not including internships\n Experience with machine learning infrastructure (e.g., GPUs, TPUs, Trainium) and associated networking infrastructure (e.g., NCCL)\n Low-level systems experience (e.g., Linux kernel tuning, eBPF)\n Experience applying security or privacy engineering best practices\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $320,000 — $485,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\n We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're eve","salary_min":320000,"salary_max":485000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["distributed-systems","alignment","cloud","research","infrastructure"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5447118008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-07T20:26:11Z","expires_at":"2026-11-13T16:54:56.973202Z","created_at":"2026-10-14T15:17:23.460298Z","updated_at":"2026-10-14T16:54:56.975982Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/84267438-bcb6-4a44-97ac-4f18bdb2ad77"},{"id":"0d92accb-86d4-4adc-8cb1-271641d5df4a","company_id":"e3915539-5a8f-4461-9f26-06366a918674","title":"Mission Software Engineer, Mission Systems, Deployment and Infrastructure","slug":"mission-software-engineer-mission-systems-deployment-and-infrastructure-11f93080","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 Air Dominance \u0026 Strike (AD\u0026S) is responsible for autonomous robotics systems like the Fury unmanned fighter jet and the Barracuda family of advanced effects. The AD\u0026S Mission Systems Deployment \u0026 Infrastructure team owns the path our software takes from a developer's commit to running on a vehicle in the field. That includes our CI/CD pipelines and build infrastructure, the software-in-the-loop (SITL) environments engineers rely on to test before deploying to hardware, and the on-vehicle Kubernetes infrastructure that manages software deployed on mission systems. A core part of this role is solving software delivery in environments that don't look like typical cloud deployment and getting builds securely into classified and disconnected government systems where standard internet-based delivery isn't an option. We are looking for engineers who want to own the full delivery chain, from build to test to field deployment, for systems where getting this wrong has real operational consequences.\n REQUIRED QUALIFICATIONS \n \n \n BS, MS, or PhD in Computer Science, Software Engineering, Mathematics, Physics, or related field.\n \n 3+ years of experience building and maintaining CI/CD pipelines and build infrastructure in a production environment\n \n Production-grade software development experience (C++, Rust, Python, Go, or equivalent) in a Linux environment\n \n Deep experience with Kubernetes and container orchestration, including deploying and managing services in constrained or non-standard network environments\n \n Comfort navigating complex, multi-stage delivery pipelines and diagnosing failures across build, test, and deployment stages\n \n Experience with Git \u0026 GitHub, including branching/release strategies for shipping software to production\n \n Travel up to 30% of time to build, test, and deploy capabilities in the real world\n Eligible to obtain and maintain an active U.S. Secret security clearance\n \n Track record of high ownership and bias for action\n \n PREFERRED QUALIFICATIONS \n \n \n Experience delivering software into classified, air-gapped, or otherwise disconnected environments\n \n Familiarity with building and maintaining software-in-the-loop (SITL) or hardware-in-the-loop (HIL) test infrastructure\n \n Experience running Kubernetes on constrained or embedded hardware (e.g., k3s or similar lightweight distributions)\n \n Experience with systemd and Linux system-level service management\n \n Experience deploying, debugging, and testing infrastructure on physical systems in both lab and test site environments\n \n Familiarity with artifact repositories, package management, and secure software supply chain practices (e.g., signed builds, provenance tracking)\n \n Familiarity with government reference architectures or accreditation processes relevant to fielding software on DoW systems\n \n Active Secret U.S. security clearance\n \n  \n US Salary Range\n $166,000 — $220,000 USD \n The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Highly competitive equity grants are included in the majority of full time offers; and are considered part of Anduril's total compensation package. Additionally, Anduril offers top-tier benefits for full-time employees, including:   \n  \n Benefits \n At Anduril, we invest in our people. Our comprehensive, competitive benefits package (available at little to no cost to employees) ensures you’re supported in health, recovery, and whatever comes next.  For more information, Explore Our Benefits . \n  \n \n Protecting Yourself from Recruitment Scams \n Anduril is committed to maintaining the integrity of our Talent acquisition process and the security of our candidates. We've observed a rise in sophisticated phishing and fraudulent schemes where individuals impersonate Anduril representatives, luring job seekers with false interviews or job offers. These scammers often attempt to extract payment or sensitive personal information.\n \n To ensure your safety and help you navigate your job search with confidence, please keep the following critical points in m","salary_min":166000,"salary_max":220000,"location":"Costa Mesa, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["cloud","payments","computer-vision","robotics","gpu","infrastructure"],"apply_url":"https://boards.greenhouse.io/andurilindustries/jobs/5260431007?gh_jid=5260431007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-07T18:48:38Z","expires_at":"2026-11-13T16:57:15.929219Z","created_at":"2026-10-14T15:21:01.067408Z","updated_at":"2026-10-14T16:57:15.931988Z","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/0d92accb-86d4-4adc-8cb1-271641d5df4a"},{"id":"36809570-dc01-41d5-87b5-5f68feb0ac68","company_id":"714f360f-a244-487d-b3f0-0c43518a9e66","title":"Sr. Staff Software Engineer, Product ML Infrastructure","slug":"sr-staff-software-engineer-product-ml-infrastructure-8c8c843d","description":"About Pinterest: \n Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.\n Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the  flexibility to do your best work. Creating a career you love? It’s Possible.\n At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.\n Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here .\n Sr. Staff Software Engineer, Product ML Infrastructure \n Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.\n At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.\n Pinterest’s Product ML Infrastructure (PMLI) team enables fast, safe, and efficient delivery of AI/ML solutions across Ads and Core critical products. We build unified data, training, feature, and inference infrastructure; this role will set technical direction across model training and serving, with a focus on GPU efficiency and large-scale ranking systems.\n What you’ll do: \n \n Set the technical vision and roadmap for model training and serving across PMLI, with reusable interfaces to data and feature infrastructure.\n Lead architectures for distributed training, fine-tuning, distillation, evaluation, and high-scale CPU/GPU inference.\n Improve efficiency across data loading, distributed execution, GPU kernels and memory, compilation, quantization, scheduling, and capacity.\n Build reliable, observable platforms with strong quality guarantees and training/serving consistency.\n Partner with Ads and Core AI/ML teams to productionize features and models safely at Pinterest scale.\n Drive cross-organizational architecture decisions, migrations, and operational standards; mentor senior engineers and raise the engineering bar.\n Use AI-assisted development and analysis to accelerate prototyping, performance diagnosis, and validation while maintaining rigorous correctness and data safeguards.\n \n What we’re looking for: \n \n A track record of setting technical strategy and delivering company-wide infrastructure initiatives in ambiguous environments.\n Deep expertise in distributed ML systems, including production experience with both large-scale training and online inference.\n Strong GPU performance knowledge, such as profiling, distributed execution, kernel and memory optimization, compilation, or quantization.\n Experience with AI/ML modeling, recommender systems, Ads ranking, retrieval, feature platforms, or similarly demanding ML workloads.\n Strong systems programming and design skills in C++, Java, or Python.\n High ownership and sound judgment in reliability, security, cost, and operational excellence.\n Demonstrated ability to use AI to improve speed and critically evaluate AI-assisted work, with accountability for correctness, quality, and sensitive data.\n Bachelor’s degree in Computer Science, Engineering, a related field, or equivalent experience.\n \n  \n Relocation Statement: \n \n This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.\n \n  \n In-Office Requirement Statement: \n \n We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.\n This role will need to be in the office for in-person collaboration 1–2 times per quarter and therefore needs to be within a commutable distance of our Palo Alto, CA or San Francisco, CA office.\n \n  \n #LI-REMOTE\n #LI-AG8\n At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Fina","salary_min":245402,"salary_max":429454,"location":"Palo Alto, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["distributed-systems","fine-tuning","data-engineering","machine-learning","infrastructure"],"apply_url":"https://www.pinterestcareers.com/jobs/?gh_jid=8046860","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-30T19:41:55Z","expires_at":"2026-11-13T16:57:51.190315Z","created_at":"2026-10-14T15:21:31.42495Z","updated_at":"2026-10-14T16:57:51.193465Z","company_name":"Pinterest","company_slug":"pinterest","company_logo_url":"https://www.google.com/s2/favicons?domain=www.pinterest.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/36809570-dc01-41d5-87b5-5f68feb0ac68"},{"id":"231dd3ff-7e25-4bb7-9a76-3cb68476e1a8","company_id":"f5ee7284-a657-4da2-b351-cb806a3681cd","title":"Member of Technical Staff - Evaluation Infrastructure","slug":"member-of-technical-staff-evaluation-infrastructure-eeccc78b","description":"SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars. SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next.\n ABOUT THE ROLE: \n The RL infrastructure team is looking for an engineer to help develop our evaluation infrastructure.\n RESPONSIBILITIES: \n \n Build highly reliable, efficient, and easy to use infrastructure that runs all our evaluations at scale\n Collaborate closely with modeling \u0026 evaluation teams on developing new evals, maintaining evaluation signal quality, and building internal tooling to support training our next-generation of models\n Identify and resolve performance bottlenecks in all layers of the eval infrastructure stack, including inference, capacity fleet management, asynchronous eval orchestration, and more\n \n BASIC QUALIFICATIONS: \n \n Experience in building, debugging, and optimizing efficiency of large-scale distributed systems\n Willingness to dive deep and solve hard problems at all levels of the stack\n \n PREFERRED SKILLS AND EXPERIENCE: \n \n Experience in LLM inference\n Experience in GPU compute management, workload scheduling, and dynamic resource optimization\n Experience in developing interfaces for comparing models and evals\n \n COMPENSATION AND BENEFITS: \n $180,000 - $440,000 USD \n Base salary is just one part of our total rewards package at SpaceXSI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short \u0026 long-term disability insurance, life insurance, and various other discounts and perks. \n SpaceXSI is an equal opportunity employer. For details on data processing, view our  Recruitment Privacy Notice .","salary_min":180000,"salary_max":440000,"location":"Palo Alto, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","distributed-systems","infrastructure","evaluation"],"apply_url":"https://job-boards.greenhouse.io/xai/jobs/5252714007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-30T19:41:34Z","expires_at":"2026-11-13T16:55:31.67405Z","created_at":"2026-10-14T15:19:21.609414Z","updated_at":"2026-10-14T16:55:31.676983Z","company_name":"xAI","company_slug":"xai","company_logo_url":"https://www.google.com/s2/favicons?domain=x.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/231dd3ff-7e25-4bb7-9a76-3cb68476e1a8"},{"id":"2d20090b-e536-4e32-8e91-f25675ac4950","company_id":"92df3417-f362-4f1a-9406-e34d8013b283","title":"AI Evaluation Infrastructure Engineer","slug":"ai-evaluation-infrastructure-engineer-4ec228d5","description":"It all started with an idea at Block in 2013. Initially built to take the pain out of peer-to-peer payments, Cash App has gone from a simple product with a single purpose to a dynamic ecosystem, developing unique financial products, including Afterpay/Clearpay, to provide a better way to send, spend, invest, borrow and save to our 50+ million monthly active customers. We want to redefine the world’s relationship with money to make it more relatable, instantly available, and universally accessible. Today, Cash App has thousands of employees working globally across office and remote locations, with a culture geared toward innovation, collaboration and impact. We’ve been a distributed team since day one, and many of our roles can be done remotely from the countries where Cash App operates. No matter the location, we tailor our experience to ensure our employees are creative, productive, and happy.\n The Role \n We build AI products, and the quality of our evaluations sets the ceiling for how good those products can be. The speed of our evaluations determines how quickly we can improve them.\n We are looking for an engineer to build the infrastructure and tooling that make high-quality AI evaluation possible at Block's scale. You will help teams understand whether a model or product change is actually better, whether a result is statistically meaningful, and whether offline evaluation is predicting what happens with real users.\n Our evaluation approach combines offline evals that encode our definition of a good response, online evals that show how people actually respond, and a feedback loop that keeps the two converging. Your work will turn that approach into systems that product teams can use quickly, reliably, and with confidence.\n This is a high-impact, early-stage area with broad surface area. You will help decide what to build first, then build the platform that helps teams ship better AI products faster.\n You Will \n \n Build an execution engine that can score candidate versions against task sets in minutes, not hours.\n Create task set tooling that samples from production logs and validates tasks before they are admitted into an evaluation set.\n Build grader infrastructure across ground truth checks, rubrics, and LLM-as-judge approaches.\n Develop tooling that helps human reviewers calibrate judges, measure judge-to-human agreement, and monitor drift over time.\n Build leaderboards and reporting systems that include sample size, confidence intervals, and run-to-run variance, so teams can distinguish real improvements from noise.\n Support in-product side-by-side serving, feedback capture, and implicit signal extraction from real conversations.\n Build the loop that compares offline scores with online outcomes, identifies eval sets that have stopped predicting reality, and helps teams improve them.\n Partner with product, engineering, data, and ML teams to make evaluation workflows fast enough and trustworthy enough to become part of everyday development.\n \n You Have \n \n Experience building production platforms or infrastructure, including distributed batch execution, data pipelines, or systems that process production logs.\n Strong statistical literacy, including comfort with confidence intervals, variance, power, and multiple comparisons.\n The judgment to identify when a result is meaningful and when it is noise.\n Experience evaluating LLM or ML systems, or deep systems engineering experience with a strong interest in AI evaluation.\n Product instinct for internal tools. You understand that leaderboards, annotation tools, and workflows only matter if teams actually use them.\n A bias toward building reliable, observable systems that other engineers can trust.\n Strong collaboration skills and the ability to work across ambiguous product, data, and engineering problems.\n \n Success in your first year \n \n Product teams can stand up credible evals for new AI surfaces in days.\n Eval results gate CI and run quickly enough that engineers do not route around them.\n Judge-to-human agreement is measured, published, and monitored for drift.\n Launch decisions are not made on results that are within statistical noise.\n At least one case is documented where online reality disagreed with offline scores, and the evaluation was improved as a result.\n \n Why this matters \n AI product development moves quickly, but speed only helps when teams can trust the signal they are using to make decisions. This role will build the systems that make those signals faster, more accurate, and more actionable. Your work will directly influence how Block evaluates, improves, and ships AI products.\n  \n Block takes a market-based approach to pay, and pay may vary depending on your location. U.S. locations are categorized into one of four zones based on a cost of labor index for that geographic area. The successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditio","salary_min":263600,"salary_max":395400,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","data-pipeline","payments","data-science","infrastructure","evaluation"],"apply_url":"http://block.xyz/careers/jobs/5434157008?gh_jid=5434157008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-30T18:33:32Z","expires_at":"2026-11-13T16:58:11.19989Z","created_at":"2026-10-14T15:21:51.219903Z","updated_at":"2026-10-14T16:58:11.202872Z","company_name":"Block","company_slug":"block","company_logo_url":"https://www.google.com/s2/favicons?domain=block.xyz\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/2d20090b-e536-4e32-8e91-f25675ac4950"},{"id":"c3e4ef9c-03db-471a-a384-685b963ad567","company_id":"a0000000-0000-0000-0000-000000000001","title":"Senior+ Software Engineer, Legal Tech","slug":"senior-software-engineer-legal-tech-2d4cba53","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the role\n Anthropic's Legal Tech engineers play a specialized and critical role in keeping Claude at the frontier. Partnering closely with counsel, we provide custom infrastructure and guidance for complex, novel technical matters, empowering teams across the company to continue meeting our legal obligations as we innovate.\n As a Senior+ Software Engineer on our Legal Tech team, you'll build dedicated, highly secure, and reliable cloud environments and data processing and storage capabilities at speed and scale. You will own your scope end to end: from raw requirements all the way through to large-scale production systems that satisfy legal invariants and unlock new possibilities. The systems you build won't just be dependable and scalable; they'll be interpretable and auditable, demonstrating to skeptical audiences exactly how they worked and why results can be trusted.\n You'll be an essential member of a collaborative team, working together to solve difficult problems against fixed deadlines. You will own internal customer relationships as well: you'll gather, prioritize, and coordinate needs from colleagues on Privacy, Security, Research, and/or Infra as well as Legal, to ship the right capabilities on the right timelines. You'll act as a trusted advisor: clearly explaining technical concepts and constraints to inform legal decisions, and proactively designing new approaches to prevent classes of problems. Your infrastructure expertise will directly empower our business to calibrate and manage risk, innovating faster thanks to systems that build in legal considerations.\n Key responsibilities\n \n \n Build and operate dedicated cloud infrastructure, storage, and data pipelines for specialized workloads that can't run on general-purpose systems\n \n Design architectures with excellent security, privacy, reliability, scalability, and auditability baked in\n \n Partner with a wide range of internal customer teams to translate requirements into sound approaches and realistic ETAs\n \n Deliver against fixed deadlines\n \n Support complex and novel legal requests on controlled infrastructure, with verifiable chain of custody\n \n Pair with engineering teams across the company to extend existing infrastructure to support new workflows and/or legal requests\n \n Work with nontechnical stakeholders to clarify their needs through responsive, easy-to-follow dialogue\n \n Minimum qualifications\n \n \n Experience designing, building, and operating highly trusted infrastructure and data systems in production, including owning architectural decisions\n \n Experience working across large, complex, multi-team environments, including in stacks and code you don't own\n \n Ability to take ownership of a cloud account and secure it end to end (networking, storage, identity, encryption, least-privilege provisioning, etc.) on AWS and/or GCP\n \n Ability to translate legal and privacy requirements into sound technical designs and implementations that teams are excited to adopt\n \n A habit of verifying rather than assuming: designing checks for invariants and plainly naming what isn't proved\n \n Self-directed in a high-change environment, and comfortable resequencing quickly when new needs emerge\n \n Clear communication with nontechnical stakeholders, and an understanding of their needs\n \n Preferred qualifications\n \n \n Software engineering experience with an infrastructure, data, privacy, and/or security focus\n \n Proficiency with large-scale data tooling, such as distributed processing, lakehouse table formats, and/or query engines\n \n Understanding of data processing pipelines, LLM training, and/or provenance and lineage\n \n Experience with secure enclaves, confidential computing, clean rooms, or other purpose-built environments for sensitive data\n \n Background in eDiscovery, litigation support, or records retention at scale, including chain of custody and verifiable integrity\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $320,000 — $485,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybrid policy: Currently, we expect all ","salary_min":320000,"salary_max":485000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["alignment","llm","cloud","data-pipeline","legal","infrastructure"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5435665008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-29T01:56:42Z","expires_at":"2026-11-13T16:54:56.468394Z","created_at":"2026-10-14T15:17:22.938503Z","updated_at":"2026-10-14T16:54:56.472145Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/c3e4ef9c-03db-471a-a384-685b963ad567"},{"id":"901acfd2-6d66-4082-b7e6-b0ba1754c580","company_id":"a0000000-0000-0000-0000-000000000001","title":"Staff + Sr. Software Engineer, Cloud Inference","slug":"staff-sr-software-engineer-cloud-inference-b11fdf49","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the role \n The Cloud Inference team scales and optimizes Claude to serve the massive audiences of developers and enterprise companies across AWS, GCP, Azure, and future cloud service providers (CSPs). We own the end-to-end product of Claude on each cloud platform, from API integration and intelligent request routing to inference execution, capacity management, and day-to-day operations.\n Our engineers are extremely high leverage: we simultaneously drive multiple major revenue streams while optimizing one of Anthropic's most precious resources: compute. As we expand to more cloud platforms, the complexity of managing inference efficiently across providers with different hardware, networking stacks, and operational models grows significantly. We need product-minded backend engineers who can navigate these platform differences, design the services and abstractions that work across providers, and make architectural decisions that keep us reliable and cost-effective at massive scale.\n Your work will increase the scale at which our services operate, accelerate our ability to reliably launch new frontier models and innovative features to customers across all platforms, and ensure our LLMs meet rigorous safety, performance, and security standards.\n Key responsibilities \n \n Design, build, and own backend services and infrastructure that serve Claude across multiple CSPs, accounting for differences in compute hardware, networking, APIs, and operational models\n Work cross-functionally with internal inference, product API, systems, and security teams, among others, and with CSP partners to stand up the full serving stack on new cloud platforms, resolve operational issues, and influence provider roadmaps\n Build and evolve CI/CD automation systems, including validation and deployment pipelines, that reliably ship new model versions to millions of users across cloud platforms without regressions\n Design interfaces and tooling abstractions across CSPs that enable cost-effective inference management, scale across providers, and reduce per-platform complexity\n Contribute to capacity planning, autoscaling, and workload routing strategies that match supply with demand and direct requests to the most cost-effective accelerator and region\n Analyze observability data across providers to identify performance bottlenecks, cost anomalies, and regressions, and drive remediation based on real-world production workloads\n \n Minimum qualifications \n \n Have significant software engineering experience, with a strong background in high-performance, large-scale distributed systems serving millions of users\n Have experience building or operating services on at least one major cloud platform (AWS, GCP, or Azure), with exposure to Kubernetes, Infrastructure as Code, or container orchestration\n Are curious about LLM serving; prior inference or ML experience is not required\n Thrive in cross-functional collaboration with both internal teams and external partners\n Have experience working with external partners to align goals and deliver impact\n Are a fast learner who can quickly ramp up on new technologies, hardware platforms, and provider ecosystems\n Are highly autonomous and take ownership of problems end-to-end, including work that falls outside your job description\n \n Preferred qualifications \n \n Direct experience working with CSPs to scale infrastructure or products across multiple platforms, navigating differences in networking, security, privacy, billing, and managed service offerings\n Hands-on experience with capacity management, cost optimization, or resource planning at scale across heterogeneous environments\n Solid understanding of multi-region deployments, geographic routing, and global traffic management\n Proficiency in Python or Rust\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $320,000 — $485,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n Vis","salary_min":320000,"salary_max":485000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["distributed-systems","payments","llm","alignment","infrastructure","inference"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5436684008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-28T18:53:28Z","expires_at":"2026-11-13T16:54:57.029878Z","created_at":"2026-10-14T15:17:23.514386Z","updated_at":"2026-10-14T16:54:57.033374Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/901acfd2-6d66-4082-b7e6-b0ba1754c580"},{"id":"c8280610-a672-4bc7-9693-7e8cdab09b06","company_id":"a0000000-0000-0000-0000-000000000001","title":"Staff + Sr. Software Engineer, Cloud Inference Launch Engineering","slug":"staff-sr-software-engineer-cloud-inference-launch-engineering-435acf86","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the role\n The Cloud Inference team scales and optimizes Claude to serve the massive audiences of developers and enterprise companies across AWS, GCP, Azure, and future cloud service providers (CSPs). We own the end-to-end product of Claude on each cloud platform, from API integration and intelligent request routing to inference execution, capacity management, and day-to-day operations.\n Within Cloud Inference, the model \u0026 inference launch team owns the validation pipeline for our inference server and load balancer on these platforms. We're responsible for every inference change — model launches, performance improvements, safeguard integrations — landing on cloud platforms with correctness, performance, and reliability intact.\n This is high-leverage infrastructure work: validation has to be fast and cheap enough to run on the same accelerators that serve customers, trustworthy enough to replace manual checks, and consistent enough that a change working on Anthropic first-party means it works everywhere. This directly determines how fast frontier models and features ship to every cloud platform, and how quickly performance wins reach production — reclaiming capacity at a time when compute is our scarcest resource.\n Key responsibilities\n \n Be on the critical path for frontier model launches, bringing up inference for new model architectures and shipping them to cloud platforms in lockstep with our first-party platform\n Work with the core inference team to bring new inference features (e.g. structured sampling, prompt caching, and more) to cloud platforms, owning the platform-specific integration that gets them to production\n Identify and dive deep on the gaps that make inference behave differently across first-party and CSPs — config drift, observability, deployment patterns, hard cross-platform bugs — and fix them at the source rather than building platform-specific workarounds\n Design, build, and own the CI/CD infrastructure for the inference server and load balancer across cloud platforms, with shadow traffic, performance baselines (throughput and latency), and correctness checks that catch regressions before production\n Drive down merge-to-production cycle time by making validation faster, more parallel, and cost-effective enough to run on the same constrained accelerator pool that serves customers, without trading away reliability \n Analyze observability data across providers to identify performance bottlenecks, cost anomalies, and regressions, and drive remediation based on real-world production workloads\n \n Minimum qualifications\n \n Have a strong interest in LLM serving; prior inference or ML experience is not required \n Have significant software engineering experience, with a strong background in high-performance, large-scale distributed systems serving millions of users\n Have a track record of building automation or test infrastructure that measurably improved release velocity or reliability\n Have experience building or operating services on at least one major cloud platform (AWS, GCP, or Azure), with exposure to Kubernetes, Infrastructure as Code, or container orchestration\n Thrive in cross-functional collaboration with both internal teams and external partners\n Are a fast learner who can quickly ramp up on new technologies, hardware platforms, and provider ecosystems\n Are highly autonomous and take ownership of problems end-to-end, including work that falls outside your job description\n \n Preferred qualifications\n \n LLM inference optimization, batching, and caching strategies\n Capacity-constrained scheduling or shared-resource test infrastructure\n Solid understanding of multi-region deployments, request routing, load balancing, global traffic management\n Working with CSP partner teams to scale infrastructure across multiple platforms, navigating differences in networking, security, privacy, and managed service\n Proficiency in Python or Rust\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $320,000 — $485,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybri","salary_min":320000,"salary_max":485000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["alignment","llm","distributed-systems","inference","infrastructure"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5436697008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-28T18:52:20Z","expires_at":"2026-11-13T16:54:57.035074Z","created_at":"2026-10-14T15:17:23.523759Z","updated_at":"2026-10-14T16:54:57.037886Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/c8280610-a672-4bc7-9693-7e8cdab09b06"},{"id":"9056f48f-8543-4763-8844-7b0844de040f","company_id":"a0000000-0000-0000-0000-000000000001","title":"Staff + Sr. Software Engineer, Scaling","slug":"staff-sr-software-engineer-scaling-e2d062ae","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the role\n Our Inference team is responsible for building and scaling the critical systems that serve Claude to millions of users worldwide. We bring Claude to life by serving our models via the industry’s largest compute-agnostic inference deployments. We are responsible for the entire stack from intelligent request routing to fleet-wide orchestration across diverse AI accelerators.\n The team has a dual mandate: maximizing compute efficiency to reliably serve our explosive customer growth, while enabling breakthrough research by giving our scientists the high-performance inference infrastructure they need to develop next-generation models. We tackle complex, distributed systems challenges across multiple accelerator families and emerging AI hardware running in multiple cloud platforms.\n Inference systems are highly performance sensitive distributed systems. Inference serves hundreds of thousands of customers every day, and the size \u0026 span of the inference fleet requires sophisticated routing, scaling, and networking systems.\n Key responsibilities\n \n Design, build, and maintain the distributed systems that serve Claude to millions of users worldwide\n Develop resilient, flexible systems that adapt in real time to real world events\n Develop intelligent request routing, load balancing, and traffic management systems across thousands of accelerators and multiple cloud providers\n Maximize compute efficiency and optimize cost across the fleet by autoscaling and orchestrating production, research, and experimental workloads across multiple cloud providers\n Build and operate production-grade deployment pipelines for releasing new models to users\n Provide high-performance inference infrastructure that enables researchers to develop next-generation models\n Integrate new AI accelerator platforms and support inference for new model architectures\n \n Minimum qualifications\n \n Significant software engineering experience, particularly with distributed systems\n Results-oriented, with a bias towards flexibility and impact\n Willingness to pick up slack, even if it goes outside your job description\n Desire to learn more about machine learning systems and infrastructure\n Thrive in environments where technical excellence directly drives both business results and research breakthroughs\n Care about the societal impacts of your work\n \n Preferred qualifications\n \n Experience with high-performance, large-scale distributed systems\n Experience implementing and deploying machine learning systems at scale\n Experience with load balancing, request routing, or traffic management systems\n Familiarity with LLM inference optimization, batching, and caching strategies\n Experience with Kubernetes and cloud infrastructure (AWS, GCP, Azure)\n Proficiency in Python or Rust\n \n Representative projects\n \n Designing intelligent routing algorithms that optimize request distribution across many accelerators in different environments\n Autoscaling our compute fleet to dynamically match supply with demand across production, research, and experimental workloads\n Building production-grade deployment pipelines for releasing new models to millions of users reliably\n Contributing to new inference features\n Supporting inference for new model architectures\n Analyzing observability data to tune performance based on real-world production workloads\n Managing multi-region deployments and geographic routing for global customers\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $320,000 — $485,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\n We encourage you to apply even if you do not believe you meet every single qua","salary_min":320000,"salary_max":485000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["alignment","llm","distributed-systems","cloud","infrastructure"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5436703008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-28T18:50:32Z","expires_at":"2026-11-13T16:54:57.040114Z","created_at":"2026-10-14T15:17:23.530381Z","updated_at":"2026-10-14T16:54:57.043047Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9056f48f-8543-4763-8844-7b0844de040f"},{"id":"35a26375-0260-4b78-9e21-8ad9674ad9cb","company_id":"545e1785-0a1e-478e-b21c-cb7ffa7e0b88","title":"Engineering Manager, ML Training Infrastructure","slug":"engineering-manager-ml-training-infrastructure-ee163ad0","description":"Woven by Toyota is enabling Toyota’s once-in-a-century transformation into a mobility company. Inspired by a legacy of innovating for the benefit of others, our mission is to challenge the current state of mobility through human-centric innovation — expanding what “mobility” means and how it serves society.\n\nOur work centers on four pillars: AD/ADAS, our autonomous driving and advanced driver assist technologies; Arene, our software development platform for software-defined vehicles; Woven City, a test course for mobility; and Cloud \u0026 AI, the digital infrastructure powering our collaborative foundation. Business-critical functions empower these teams to execute, and together, we’re working toward one bold goal: a world with zero accidents and enhanced well-being for all.\n\n\nAbout the Team\nEnterprise AI is a platform that provides end-to-end machine learning tooling experience to support and accelerate machine learning development, including autonomous driving and other related projects. Our platform serves customers as a standardized machine learning platform within Woven by Toyota as the larger Toyota Group companies.\nThe Enterprise AI ML Training Infrastructure team builds and operates the infrastructure that enables engineers and researchers to train, evaluate, and iterate on machine learning models at scale. Our customers include teams working on AD/ADAS and Woven City, where large-scale machine learning workloads, simulation, and data processing are critical to developing the next generation of mobility technologies.\nThe team operates at the intersection of machine learning, distributed systems, cloud infrastructure, and developer platforms. Our systems include large-scale GPU compute environments, Kubernetes-based cluster infrastructure, and workflow and pipeline orchestration technologies such as Ray, Airflow, and Temporal.\nWe are looking for an Engineering Manager to lead a team to help shape the technical direction, engineering culture, and long-term evolution of our ML training infrastructure platform.\n \nWHO ARE WE LOOKING FOR?\nAs the Engineering Manager, ML Training Infrastructure, you will lead the team responsible for building and operating the infrastructure that powers machine learning training workloads across Woven by Toyota.\nYou will combine engineering leadership with strong technical judgment. You will work closely with engineering, research, and product stakeholders to understand customer needs, translate them into scalable technical solutions, and ensure that our platform is reliable, efficient, secure, and easy to use.\nThis role is particularly suited to an engineering leader who enjoys working on complex infrastructure problems and is comfortable operating across Kubernetes, distributed systems, GPU infrastructure, cloud platforms, and machine learning workloads.\nYou will also play an important role in building a strong engineering culture, developing engineers, establishing effective engineering practices, and partnering with our customers to continuously improve the platform.\n \nWhat You Will Enable\nIn this role, you will help build the infrastructure that enables teams across Woven by Toyota to train and operate increasingly large and sophisticated machine learning workloads.\nYour work will directly support engineers and researchers working on AD/ADAS, Woven City, and other mobility initiatives by making compute resources easier to access, workloads more reliable, and ML development faster and more scalable.\nYou will have the opportunity to shape both the technical architecture of a critical ML infrastructure platform and the engineering culture of the team building it.\n","salary_min":140000,"salary_max":260000,"location":"Palo Alto, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["gpu","distributed-systems","cloud","autonomous-vehicles","infrastructure"],"apply_url":"https://jobs.lever.co/woven-by-toyota/b7844018-80f2-4d52-9749-496cf50cb182/apply","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-28T17:26:53.682Z","expires_at":"2026-11-13T17:00:17.974246Z","created_at":"2026-10-14T15:23:51.72398Z","updated_at":"2026-10-14T17:00:17.977194Z","company_name":"Woven by Toyota","company_slug":"woven-toyota","company_logo_url":"https://www.google.com/s2/favicons?domain=woven.toyota\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/35a26375-0260-4b78-9e21-8ad9674ad9cb"},{"id":"0f2fb590-511c-4b74-ae3b-072c953ce7dc","company_id":"c587b06c-b6f0-4d1d-b694-6fb6abc2a6bb","title":"Senior Product Manager, AI Infrastructure","slug":"senior-product-manager-ai-infrastructure-c983744f","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 The Way We Work\n The people who thrive here are builders who move fast, communicate openly, take ownership, and continuously improve themselves, their teams, and our company. Here's what that looks like in practice:\n \n Move with Urgency: We move quickly, make thoughtful decisions, and keep momentum. We value action over perfection and learn by shipping.\n Take Ownership: We own outcomes, not just our individual work. We make decisions that move the company forward and follow through.\n Communicate Openly: We communicate directly, seek to understand, and create clarity for others. Honest conversations help us move faster together.\n Build Great Teams: We lead by example, empower others, and create healthy teams where people can do their best work.\n Raise the Bar: We're always improving ourselves. We learn from feedback, consistently challenge ourselves to grow, and focus on the work that matters most.\n Think Long-Term: We design for what's next. We create scalable systems, simplify complexity, and use AI and automation to amplify our impact.\n \n  \n Who We're Looking For \n We’re looking for a Product Manager, AI Infrastructure to own the product experience for Lightning AI’s GPU cloud, from capacity and provisioning through workload execution, reliability, observability, and customer consumption.\n This role sits at the intersection of AI infrastructure, cloud platforms, and developer experience. You’ll define how customers discover, provision, configure, and operate GPU compute, while partnering closely with infrastructure engineering and operations to make the platform more reliable, efficient, and scalable.\n The right candidate understands that infrastructure itself is the product. You should be comfortable reasoning about GPU availability and utilization, cluster provisioning, networking and storage, scheduling and orchestration, workload reliability, and the APIs and workflows that expose these systems to customers.\n You’ll work across Engineering, Infrastructure, Sales, customers, and the executive team to determine what we build, how infrastructure capabilities are exposed and packaged, and where Lightning AI can differentiate from hyperscalers and other GPU clouds.\n This is a high-ownership role. You’ll investigate infrastructure and customer problems directly, use data to understand reliability and utilization, make technical tradeoffs with engineers, and drive products from problem definition through launch, adoption, and iteration.\n You’ll join the Product team and report to our VP of Product. This is a hybrid role based in New York City or San Francisco, with an in-office expectation of two days per week.\n What You’ll Do \n \n Own the product vision and roadmap for Lightning AI’s GPU cloud infrastructure.\n Define how customers discover, provision, configure, and consume GPU compute.\n Build product experiences around GPU capacity, clusters, scheduling, networking, storage, and workload execution.\n Partner closely with infrastructure and platform engineering to improve availability, reliability, utilization, and performance.\n Develop a deep understanding of customer workloads, from experimentation and training through inference, and translate those needs into infrastructure capabilities.\n Use infrastructure and product data to identify capacity constraints, reliability issues, performance bottlenecks, and opportunities to improve the customer experience.\n Define the APIs, interfaces, and developer workflows through which customers interact with infrastructure.\n Make product tradeoffs across customer experience, infrastructure efficiency, reliability, cost, and engineering complexity.\n Own pricing, packaging, and consumption models in partnership with Ops, Sales, and Finance.\n Partner with GTM on positioning, technical sales conversations, customer feedback, and competitive differentiation.\n Define and track metrics across GPU utilization, provisioning, workload reliability, infrastructure consumption, adoption, and retention.\n Take products from problem discovery through requirements, launch, adoption, and iteration.\n \n What You’ll Need \n \n 7+ years of product management experience, including 3+ years building cloud infrastruct","salary_min":200000,"salary_max":250000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["distributed-systems","cloud","gpu","pytorch","infrastructure"],"apply_url":"https://job-boards.greenhouse.io/lightningai/jobs/8004138003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-24T21:45:59Z","expires_at":"2026-11-13T16:55:32.809533Z","created_at":"2026-10-14T15:19:22.897957Z","updated_at":"2026-10-14T16:55:32.813252Z","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/0f2fb590-511c-4b74-ae3b-072c953ce7dc"},{"id":"0030b33b-6853-4e42-98f1-d054f946d52f","company_id":"e3915539-5a8f-4461-9f26-06366a918674","title":"Senior Software Engineer, Simulation Infrastructure","slug":"senior-software-engineer-simulation-infrastructure-459db0a7","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 WHY WE ARE HERE: Air Dominance \u0026 Strike (AD\u0026S) is responsible for autonomous robotics systems like the Fury unmanned fighter jet and the Barracuda family of advanced effects. The AD\u0026S Group 5 Simulation team is responsible for developing the simulation capabilities, infrastructure, and analysis tools developers use every day to analyze thousands of hours of autonomous flight time every day before flying software on a real vehicle. We are looking for engineers excited to develop foundational simulation capabilities for current and future Group 5 vehicle programs. WHAT YOU'LL DO \n \n Scaling simulation infrastructure for training ML models / quantifying robotics performance\n Cloud-based ETL pipeline infrastructure (Spark, Flyte, Airflow)\n Automated / generative simulation environments for robotics testing\n Developing Python tooling for simulation infrastructure / orchestration\n \n REQUIRED QUALIFICATIONS \n \n Prior experience in autonomous systems, aerospace, robotics, or other mission-critical domains\n Extensive experience developing simulation tools and infrastructure for leading commercial AV companies\n Extensive experience with cloud platforms (AWS, Azure, or GCP) and understanding of their core services.\n \n PREFERRED QUALIFICATIONS \n \n Experience with embedded Linux systems development and associated tools.\n Experience troubleshooting and analyzing remotely deployed software systems.\n Familiarity with monitoring and logging tools (like auditd, journald, selinux, Splunk).\n Extensive experience with cloud platforms (AWS, Azure, or GCP) and understanding of their core services.\n US Salary Range\n $191,000 — $253,000 USD \n The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Highly competitive equity grants are included in the majority of full time offers; and are considered part of Anduril's total compensation package. Additionally, Anduril offers top-tier benefits for full-time employees, including:   \n  \n Benefits \n At Anduril, we invest in our people. Our comprehensive, competitive benefits package (available at little to no cost to employees) ensures you’re supported in health, recovery, and whatever comes next.  For more information, Explore Our Benefits . \n  \n \n Protecting Yourself from Recruitment Scams \n Anduril is committed to maintaining the integrity of our Talent acquisition process and the security of our candidates. We've observed a rise in sophisticated phishing and fraudulent schemes where individuals impersonate Anduril representatives, luring job seekers with false interviews or job offers. These scammers often attempt to extract payment or sensitive personal information.\n \n To ensure your safety and help you navigate your job search with confidence, please keep the following critical points in mind:\n \n \n No Financial Requests:  Anduril will never solicit payment or demand personal financial details (such as banking information, credit card numbers, or social security numbers) at any stage of our hiring process. Our legitimate recruitment is entirely free for candidates.\n Please always verify communications: \n \n Direct from Anduril: If you receive an email from one of our recruiters, it will only come from an @anduril.com address.\n Via Agency Partner: If contacted by a recruiting agency for an Anduril role, their email will clearly identify their agency. If you suspect any suspicious activity, please verify the agency's authenticity by reaching out to contact@anduril.com . \n \n \n Exercise Caution with Unsolicited Outreach:  If you receive any communication that appears suspicious, contains grammatical errors, or makes unusual requests, do not engage. Always confirm the sender's email domain is @anduril.com before providing any personal information or clicking on links.\n \n What to Do If You Suspect Fraud:  Should you encounter any questionable or fraudulent outreach claiming to be from Anduril, please report it immediately to  contact@anduril.com . Your proactive caution is invaluable in protecting your personal information and upholding the securit","salary_min":191000,"salary_max":253000,"location":"Costa Mesa, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["computer-vision","data-pipeline","cloud","gpu","payments","robotics","infrastructure"],"apply_url":"https://boards.greenhouse.io/andurilindustries/jobs/5217318007?gh_jid=5217318007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-23T21:54:42Z","expires_at":"2026-11-13T16:57:16.475284Z","created_at":"2026-10-14T15:21:01.713098Z","updated_at":"2026-10-14T16:57:16.478448Z","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/0030b33b-6853-4e42-98f1-d054f946d52f"},{"id":"4f6fe178-7279-49b3-93ea-20220b5bffe7","company_id":"b467c425-56b3-40ce-826a-e603e82a08bd","title":"Senior Software Engineer, ML Infra - Asset Safety","slug":"senior-software-engineer-ml-infra-asset-safety-200dc9f2","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 Safety and Civility is Roblox's #1 priority, and our mission is to protect our global community. As a Senior Software Engineer on the Asset Safety team, you will serve as a hands-on technical leader building resilient, highly scalable systems to ensure millions of daily user-generated assets remain safe and civil. You will tackle complex engineering challenges across diverse modalities—including 3D content, code, images, and video—operating at platform-wide scale across billions of engagement hours while maintaining efficient, low-frequency high-throughput processing.\n You will: \n \n Architect, build, and optimize end-to-end ML-based safety moderation systems to detect and mitigate problematic content using scalable distributed architectures and event-driven, asynchronous workflows.\n Deliver production-grade, AI-driven safety products end-to-end in a dynamic environment, driving execution from initial ideation to deployment while aligning key cross-functional stakeholders.\n Drive technical roadmap decisions, implement robust observability and reliability practices, integrate strict security and compliance standards, and enforce rigorous testing and CI/CD pipelines while mentoring engineering team members.\n Partner closely with Moderation Operations, Product Management, Data Science, and partner engineering teams across Roblox to deliver cohesive safety solutions.\n \n You have: \n \n 5+ years of professional software engineering experience designing, building, and deploying large-scale ML infrastructure and distributed production systems.\n Deep backend engineering expertise in high-capacity microservices, advanced distributed architecture, scalable data storage and management, and event-driven asynchronous processing.\n Proven technical leadership with a track record of architecting, building, and deploying low-latency, distributed ML services to production end-to-end.\n Practical experience with streaming and messaging platforms (e.g., Apache Kafka, Apache Flink), end-to-end testing, CI/CD pipelines, security compliance, and comprehensive system observability.\n Hands-on experience with computer vision techniques and multi-modal architectures spanning image, audio, video, and 3D content modeling.\n Experience fine-tuning large Vision-Language Models (VLMs) is a plus.\n \n You are: \n \n Resourceful: Adaptable in fast-moving environments, with a track record of driving solutions through complex challenges.\n Analytical: Driven to investigate large, complex, and ambiguous technical challenges, using data to quantify problem scope and measure solution effectiveness.\n User-Oriented: Committed to prioritizing the needs of platform users and internal technical partners when designing and scaling backend architecture.\n Team-Oriented: Dedicated to elevating engineering peers, serving as an active mentor, and cultivating a high-standard culture of engineering excellence.\n Mission-Oriented: Focused on core organizational goals, bringing strategic foresight and adaptability to balance long-term technical vision with immediate business impact.\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 variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual salary could fall outside of this expected range. This pay range is subject to change and may be modified in the future. All full-time employees are also eligible for equity compensation and for benefits as described on this page .\n Annual Salary Range\n $278,530 — $328,610 USD \n Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted).\n Roblox provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characterist","salary_min":278530,"salary_max":328610,"location":"San Mateo, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["microservices","fine-tuning","computer-vision","data-pipeline","infrastructure","machine-learning"],"apply_url":"https://careers.roblox.com/jobs/8224452?gh_jid=8224452","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-23T20:56:40Z","expires_at":"2026-11-13T17:02:53.272967Z","created_at":"2026-10-14T15:24:56.268225Z","updated_at":"2026-10-14T17:02:53.276063Z","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/4f6fe178-7279-49b3-93ea-20220b5bffe7"}],"page":1,"per_page":20,"total":602,"total_is_exact":true,"total_pages":31}