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Machine Learning Engineer

Owns end-to-end production ML systems for clinical workflows, including training/fine-tuning LLMs for medical reasoning and question answering. Requires strong ML/software engineering, PyTorch experience, and ability to handle high-stakes ambiguity with real patient impact.

225k – 300kSan Francisco, CAML EngineeringOnsite

About the role

What You’ll Do

  • Own end-to-end ML systems, including architecture, data, modeling, evaluation, and production infrastructure
  • Train and fine-tune large language models (LLMs) for:
    • Clinical reasoning
    • Medical question answering
    • Evidence-grounded generation
  • Make and own tradeoffs across accuracy, latency, cost, and safety in high-stakes production environments
  • Develop evaluation frameworks to ensure model safety and clinical validity
  • Integrate ML systems into product workflows and patient-facing applications
  • Monitor system performance in production and iterate based on real-world usage and feedback
  • Define what “correct” means in ambiguous clinical workflows in collaboration with engineers and clinicians

What We’re Looking For

  • Strong foundation in machine learning and software engineering
  • Track record of building and owning ML systems in production where performance, reliability, or correctness materially mattered
  • Experience driving ambiguous ML problems from 0→1, including problem formulation, model design, and productionization
  • Hands-on experience with PyTorch or similar frameworks
  • Ability to operate independently in high-ambiguity environments with minimal guidance
  • Strong product and engineering judgment — you know when to use ML, when not to, and how to scope problems accordingly
  • Comfort working in a fast-moving, early-stage environment
  • Experience working on systems where decisions have real-world consequences (e.g., healthcare, finance, infrastructure)

Nice to Have

  • Experience deploying LLMs in production environments
  • Experience building distributed systems or large-scale data pipelines
  • Experience working with clinical, biomedical, or other regulated datasets

Compensation

Base salary: $225,000 – $300,000+ Meaningful equity in an early-stage, Series A company

Skills

PyTorchLLMsReinforcement LearningMachine LearningLlm Fine-TuningDistributed SystemsData PipelinesClinical DataProduction MlEvaluation Frameworks

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