{"schemaVersion":"jobsearcher.job.v1","id":"80ff7b8c1774017ab20b3f76","url":"https://jobsearcher.com/jobs/80ff7b8c1774017ab20b3f76","canonicalUrl":"https://jobsearcher.com/jobs/80ff7b8c1774017ab20b3f76","title":"Reliability Engineer, Supercomputing","description":"Thinking Machines Lab's mission is to empower humanity through advancing collaborative general intelligence. We're building a future where everyone has access to the knowledge and tools to make AI work for their unique needs and goals.\n\nWe are scientists, engineers, and builders who’ve created some of the most widely used AI products, including ChatGPT and Character.ai, open-weights models like Mistral, as well as popular open source projects like PyTorch, OpenAI Gym, Fairseq, and Segment Anything.\n\nAbout the Role\nWe're hiring an engineer to ensure the reliability of our GPU supercomputing fleet, owning the seam between hardware, firmware, and operating system. You will track the long tail of hardware issues: We are conducting frontier research in AI and a single bad NIC, HBM or a kernel driver edge case can compromise an experiment. Your job is to diagnose these issues, track their root cause down to the hardware, and resolve them internally or directly with vendors so that our researchers can run at scale and with confidence.\n\nNote: This is an \"evergreen role\" that we keep open on an on-going basis to express interest. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months. You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you're welcome to apply directly in addition to an evergreen role.\n\nWhat You’ll Do\n\nInvestigate, reproduce, and remediate issues across large GPU clusters.\n\nOwn the drivers, kernel surface, and diagnostics that span hardware, firmware, and OS.\n\nAutomate the monitoring of fleet reliability and analyze error rates to validate whether a fix or firmware change measurably reduced failures rather than shifting them around.\n\nDrive the firmware lifecycle: tracking, qualification, staged rollout, and regression analysis.\n\nEngage vendors directly — GPUs, server OEMs, NIC vendors, and storage vendors — to get real fixes rather than ticket numbers. Manage RMA flows when hardware needs to come out.\n\nMonitor and improve GPU hardware health signals and turn them into actionable reliability improvements.\n\nWrite clear postmortems and vendor cases that move issues forward.\n\nSkills and Qualifications\nMinimum qualifications:\n\nBachelor’s degree or equivalent experience in computer science, engineering, or similar.\n\nProficiency in at least one backend language (we use Python or Rust).\n\nExperience operating large‑scale clusters and container orchestration systems (e.g. Kubernetes or Slurm).\n\nComfort operating across the stack and owning projects end-to-end.\n\nThrive in a highly collaborative environment involving many, different cross‑functional partners and subject matter experts.\n\nA bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.\n\nPreferred qualifications — we encourage you to apply if you meet some but not all of these:\n\nFluency with Linux systems and debugging tools.\n\nProven statistical rigor in analyzing reliability.\n\nA track record of debugging a problem from application symptom to the root cause in hardware.\n\nComfort reading vendor errata, firmware release notes, and kernel changelogs.\n\nExperience engaging hardware vendors directly — not just through escalation portals.\n\nLinux kernel literacy: the scheduler, memory management, IRQ paths, and the driver model.\n\nOut-of-band management experience: BMC / iDRAC / IPMI / Redfish.\n\nDepth in GPU hardware health: Xid error taxonomy, NVLink, NVSwitch, fabric manager, and DCGM.\n\nProficiency in at least one backend language (we use Python and Rust).\n\nSignificant ownership of the hardware reliability function at scale.\n\nStrong writing skills for vendor cases and postmortems.\n\nAn instinct for telling apart a flaky machine, a flaky workload, and a flaky test.\n\nLogistics\n\nLocation: This role is based in San Francisco, California.\n\nCompensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD.\n\nVisa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together.\n\nBenefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.\n\nAs set forth in Thinking Machines' Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.\n\nThinking Machines Lab will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of the California Fair Chance Act, the San Francisco Fair Chance Ordinance, and any other applicable state or local fair chance ordinance or law.\n\n#J-18808-Ljbffr","company":"Thinkingmachines","rawCompany":"thinkingmachines","city":"Millbrae","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-07-16T03:53:37.336Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"334111","title":"Electronic Computer Manufacturing","slug":"electronic-computer-manufacturing"},{"code":"518210","title":"Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services","slug":"computing-infrastructure-providers-data-processing-web-hosting-and-related-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Reliability Engineer, Supercomputing","description":"Thinking Machines Lab's mission is to empower humanity through advancing collaborative general intelligence. We're building a future where everyone has access to the knowledge and tools to make AI work for their unique needs and goals.\n\nWe are scientists, engineers, and builders who’ve created some of the most widely used AI products, including ChatGPT and Character.ai, open-weights models like Mistral, as well as popular open source projects like PyTorch, OpenAI Gym, Fairseq, and Segment Anything.\n\nAbout the Role\nWe're hiring an engineer to ensure the reliability of our GPU supercomputing fleet, owning the seam between hardware, firmware, and operating system. You will track the long tail of hardware issues: We are conducting frontier research in AI and a single bad NIC, HBM or a kernel driver edge case can compromise an experiment. Your job is to diagnose these issues, track their root cause down to the hardware, and resolve them internally or directly with vendors so that our researchers can run at scale and with confidence.\n\nNote: This is an \"evergreen role\" that we keep open on an on-going basis to express interest. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months. You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you're welcome to apply directly in addition to an evergreen role.\n\nWhat You’ll Do\n\nInvestigate, reproduce, and remediate issues across large GPU clusters.\n\nOwn the drivers, kernel surface, and diagnostics that span hardware, firmware, and OS.\n\nAutomate the monitoring of fleet reliability and analyze error rates to validate whether a fix or firmware change measurably reduced failures rather than shifting them around.\n\nDrive the firmware lifecycle: tracking, qualification, staged rollout, and regression analysis.\n\nEngage vendors directly — GPUs, server OEMs, NIC vendors, and storage vendors — to get real fixes rather than ticket numbers. Manage RMA flows when hardware needs to come out.\n\nMonitor and improve GPU hardware health signals and turn them into actionable reliability improvements.\n\nWrite clear postmortems and vendor cases that move issues forward.\n\nSkills and Qualifications\nMinimum qualifications:\n\nBachelor’s degree or equivalent experience in computer science, engineering, or similar.\n\nProficiency in at least one backend language (we use Python or Rust).\n\nExperience operating large‑scale clusters and container orchestration systems (e.g. Kubernetes or Slurm).\n\nComfort operating across the stack and owning projects end-to-end.\n\nThrive in a highly collaborative environment involving many, different cross‑functional partners and subject matter experts.\n\nA bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.\n\nPreferred qualifications — we encourage you to apply if you meet some but not all of these:\n\nFluency with Linux systems and debugging tools.\n\nProven statistical rigor in analyzing reliability.\n\nA track record of debugging a problem from application symptom to the root cause in hardware.\n\nComfort reading vendor errata, firmware release notes, and kernel changelogs.\n\nExperience engaging hardware vendors directly — not just through escalation portals.\n\nLinux kernel literacy: the scheduler, memory management, IRQ paths, and the driver model.\n\nOut-of-band management experience: BMC / iDRAC / IPMI / Redfish.\n\nDepth in GPU hardware health: Xid error taxonomy, NVLink, NVSwitch, fabric manager, and DCGM.\n\nProficiency in at least one backend language (we use Python and Rust).\n\nSignificant ownership of the hardware reliability function at scale.\n\nStrong writing skills for vendor cases and postmortems.\n\nAn instinct for telling apart a flaky machine, a flaky workload, and a flaky test.\n\nLogistics\n\nLocation: This role is based in San Francisco, California.\n\nCompensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD.\n\nVisa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together.\n\nBenefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.\n\nAs set forth in Thinking Machines' Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.\n\nThinking Machines Lab will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of the California Fair Chance Act, the San Francisco Fair Chance Ordinance, and any other applicable state or local fair chance ordinance or law.\n\n#J-18808-Ljbffr","datePosted":"2026-07-16T03:53:37.336Z","dateModified":"2026-07-16T03:53:37.336Z","hiringOrganization":{"@type":"Organization","name":"Thinkingmachines","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"80ff7b8c1774017ab20b3f76"},"url":"https://jobsearcher.com/jobs/80ff7b8c1774017ab20b3f76"}}