{"schemaVersion":"jobsearcher.job.v1","id":"2c8eb59ac05e31e078e1f58f","url":"https://jobsearcher.com/jobs/2c8eb59ac05e31e078e1f58f","canonicalUrl":"https://jobsearcher.com/jobs/2c8eb59ac05e31e078e1f58f","title":"MACHINE LEARNING ENGINEER (GENERAL)","description":"ABOUT THE COMPANYWe're building autonomous research agents for recursive self-improvement (multi-agent systems that propose, run, and analyze machine learning experiments). We're a small team based in San Francisco, on-siteABOUT THE ROLEYou'll build and maintain the ML systems and pipelines that our research runs on top of data pipelines, training infrastructure, evaluation tooling, deployment, observability. The work bridges research and production, and you'll be the person who makes \"we ran an experiment\" actually mean \"we ran it correctly, at scale, with results we trust.\"\r\nThis is a senior ML engineering role. You'll own systems end-to-end. You'll work with researchers daily and translate research code into infrastructure that the team can rely on. You'll move fast and you'll be measured on whether your systems make the team faster.\r\nWHAT YOU'LL DOBuild and maintain the training, evaluation, and deployment pipelines that our research runs on\r\nTake research code from prototype to production: refactor, harden, instrument, test\r\nDesign observability into our ML systems (metrics, logs, traces, eval dashboards) so failures surface fast\r\nOwn data pipelines for training and evaluation: ingest, dedup, version, validate\r\nWork closely with researchers to understand what they need, what's slow, and what's brittle\r\nSet engineering standards across our ML stack (testing, reviews, runbooks) so the team scales\r\nContribute to architectural decisions that shape how research and production interacts\r\nWHAT WE'RE LOOKING FOR:Senior ML engineer with 6+ years building production-grade ML systems\r\nTrack record across the full lifecycle: data, training, evaluation, deployment, monitoring\r\nStrong distributed systems experience; you've shipped systems that have to be on\r\nFluent Python, fluent with at least one of (PyTorch, JAX); comfortable at the systems-level when needed\r\nComfortable with experimentation infrastructure (Ray, Slurm, Kubernetes, or similar)\r\nBias toward shipping; you prefer working code over working diagrams\r\nStrong written communication\r\nNICE TO HAVE:Experience building experimentation platforms or research infrastructure at a frontier ML lab\r\nBackground in distributed training systems\r\nOpen-source contributions to ML infrastructure\r\nHistory of working effectively with small senior teams\r\nTHIS ROLE IS PROBABLY NOT FOR YOU IF:You want to do research with engineering as a side activity: this is engineering as the main thing\r\nCross-functional work with researchers (translation, scoping, education) doesn't appeal\r\nLong-running ownership of running systems isn't appealing: this role has it#J-18808-Ljbffr","company":"Makermakerai","rawCompany":"makermakerai","city":"Millbrae","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-10-04T03:05:45.902Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"MACHINE LEARNING ENGINEER (GENERAL)","description":"ABOUT THE COMPANYWe're building autonomous research agents for recursive self-improvement (multi-agent systems that propose, run, and analyze machine learning experiments). We're a small team based in San Francisco, on-siteABOUT THE ROLEYou'll build and maintain the ML systems and pipelines that our research runs on top of data pipelines, training infrastructure, evaluation tooling, deployment, observability. The work bridges research and production, and you'll be the person who makes \"we ran an experiment\" actually mean \"we ran it correctly, at scale, with results we trust.\"\r\nThis is a senior ML engineering role. You'll own systems end-to-end. You'll work with researchers daily and translate research code into infrastructure that the team can rely on. You'll move fast and you'll be measured on whether your systems make the team faster.\r\nWHAT YOU'LL DOBuild and maintain the training, evaluation, and deployment pipelines that our research runs on\r\nTake research code from prototype to production: refactor, harden, instrument, test\r\nDesign observability into our ML systems (metrics, logs, traces, eval dashboards) so failures surface fast\r\nOwn data pipelines for training and evaluation: ingest, dedup, version, validate\r\nWork closely with researchers to understand what they need, what's slow, and what's brittle\r\nSet engineering standards across our ML stack (testing, reviews, runbooks) so the team scales\r\nContribute to architectural decisions that shape how research and production interacts\r\nWHAT WE'RE LOOKING FOR:Senior ML engineer with 6+ years building production-grade ML systems\r\nTrack record across the full lifecycle: data, training, evaluation, deployment, monitoring\r\nStrong distributed systems experience; you've shipped systems that have to be on\r\nFluent Python, fluent with at least one of (PyTorch, JAX); comfortable at the systems-level when needed\r\nComfortable with experimentation infrastructure (Ray, Slurm, Kubernetes, or similar)\r\nBias toward shipping; you prefer working code over working diagrams\r\nStrong written communication\r\nNICE TO HAVE:Experience building experimentation platforms or research infrastructure at a frontier ML lab\r\nBackground in distributed training systems\r\nOpen-source contributions to ML infrastructure\r\nHistory of working effectively with small senior teams\r\nTHIS ROLE IS PROBABLY NOT FOR YOU IF:You want to do research with engineering as a side activity: this is engineering as the main thing\r\nCross-functional work with researchers (translation, scoping, education) doesn't appeal\r\nLong-running ownership of running systems isn't appealing: this role has it#J-18808-Ljbffr","datePosted":"2026-10-04T03:05:45.902Z","dateModified":"2026-10-04T03:05:45.902Z","hiringOrganization":{"@type":"Organization","name":"Makermakerai","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"2c8eb59ac05e31e078e1f58f"},"url":"https://jobsearcher.com/jobs/2c8eb59ac05e31e078e1f58f"}}