{"schemaVersion":"jobsearcher.job.v1","id":"fe0eb52fb07964393e2b0400","url":"https://jobsearcher.com/jobs/fe0eb52fb07964393e2b0400","canonicalUrl":"https://jobsearcher.com/jobs/fe0eb52fb07964393e2b0400","title":"Machine Learning Engineer","description":"A high-growth AI-native startup is hiring an ML Engineer in San Francisco to take models from notebook to production and keep them performant, reliable and affordable at scale.This role sits at the intersection of ML and engineering: strong Python, strong PyTorch, and the engineering maturity to turn models into real product surfaces. You’ll own training, serving, evaluation and the plumbing underneath.Location: San Francisco, 5 days per week in officeWhat you’ll be doing:Train, fine-tune and serve models in production using Python and PyTorchBuild and maintain the inference stack: model serving (vLLM, Triton, custom), batching, caching, cost controlStand up training infrastructure using Ray, Modal, SageMaker or similarWork with vector databases (pgvector, Pinecone, Weaviate) for retrieval-heavy workloadsBuild evaluation pipelines so model quality is measurable rather than anecdotalPartner with product and AI engineers to turn research or OSS models into shipped featuresOwn GPU utilisation, latency and inference cost as first-class concernsUse AI tooling (Claude, Cursor and similar) across every part of your workflowWhat you’ll need:Strong Python and PyTorch experience with real production ML systems behind youExperience with Hugging Face Transformers, modern training workflows and distributed trainingHands-on with model serving stacks (vLLM, TensorRT, Triton or equivalent) and GPU-level optimisationComfort with experiment tracking (MLflow, Weights & Biases) and reproducibility hygieneCloud experience (AWS, GCP or Azure) and modern infra toolingFluent in modern AI engineering tools and able to use them to raise outputHappy working 5 days a week in-person with a small, high-intensity teamNice to have:Experience fine-tuning or post-training LLMs (SFT, DPO, RLHF)Low-latency inference work or custom CUDA kernelsThis role would suit an ML engineer who treats the inference bill, the latency graph and the eval harness as part of their job, not someone else’s.#J-18808-Ljbffr","company":"Digital Waffle","rawCompany":"digital waffle","city":"Millbrae","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-11-04T02:09:57.104Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Machine Learning Engineer","description":"A high-growth AI-native startup is hiring an ML Engineer in San Francisco to take models from notebook to production and keep them performant, reliable and affordable at scale.This role sits at the intersection of ML and engineering: strong Python, strong PyTorch, and the engineering maturity to turn models into real product surfaces. You’ll own training, serving, evaluation and the plumbing underneath.Location: San Francisco, 5 days per week in officeWhat you’ll be doing:Train, fine-tune and serve models in production using Python and PyTorchBuild and maintain the inference stack: model serving (vLLM, Triton, custom), batching, caching, cost controlStand up training infrastructure using Ray, Modal, SageMaker or similarWork with vector databases (pgvector, Pinecone, Weaviate) for retrieval-heavy workloadsBuild evaluation pipelines so model quality is measurable rather than anecdotalPartner with product and AI engineers to turn research or OSS models into shipped featuresOwn GPU utilisation, latency and inference cost as first-class concernsUse AI tooling (Claude, Cursor and similar) across every part of your workflowWhat you’ll need:Strong Python and PyTorch experience with real production ML systems behind youExperience with Hugging Face Transformers, modern training workflows and distributed trainingHands-on with model serving stacks (vLLM, TensorRT, Triton or equivalent) and GPU-level optimisationComfort with experiment tracking (MLflow, Weights & Biases) and reproducibility hygieneCloud experience (AWS, GCP or Azure) and modern infra toolingFluent in modern AI engineering tools and able to use them to raise outputHappy working 5 days a week in-person with a small, high-intensity teamNice to have:Experience fine-tuning or post-training LLMs (SFT, DPO, RLHF)Low-latency inference work or custom CUDA kernelsThis role would suit an ML engineer who treats the inference bill, the latency graph and the eval harness as part of their job, not someone else’s.#J-18808-Ljbffr","datePosted":"2026-11-04T02:09:57.104Z","dateModified":"2026-11-04T02:09:57.104Z","hiringOrganization":{"@type":"Organization","name":"Digital Waffle","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"fe0eb52fb07964393e2b0400"},"url":"https://jobsearcher.com/jobs/fe0eb52fb07964393e2b0400"}}