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Research Scientist

Owns end-to-end ML research initiatives developing novel architectures, training methods, and evaluation for clinical intelligence using longitudinal patient data. Requires strong ML foundation, PyTorch experience, and ability to drive ambiguous high-stakes problems to validated results.

225k – 300kSan Francisco, CAML EngineeringOnsite

About the role

What You’ll Do

  • Own research initiatives end-to-end, including problem formulation, experimental design, modeling, and evaluation
  • Develop novel architectures, training methods, and objectives leveraging longitudinal patient data
  • Work on verifiable reinforcement learning, mid-training, and post-training of foundation models
  • Design rigorous evaluation methodologies to assess model reasoning, correctness, and clinical relevance
  • Make and own tradeoffs between model capability, interpretability, and verifiability in high-stakes settings
  • Collaborate with clinicians and engineers to define meaningful problem formulations grounded in real-world workflows
  • Partner with ML engineers to ensure research translates into deployable systems

What We’re Looking For

  • Strong foundation in machine learning, deep learning, or a related technical field
  • Track record of driving ML research or novel modeling work from idea to validated results
  • Experience working on ambiguous research problems with limited prior art
  • Hands-on experience with PyTorch or similar frameworks
  • Ability to operate independently in high-ambiguity environments with minimal guidance
  • Strong technical judgment — you can identify meaningful problems, design appropriate approaches, and evaluate results rigorously
  • 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

  • Publications at top-tier ML venues (e.g., NeurIPS, ICML, ICLR)
  • Experience with LLMs, NLP, or sequence modeling
  • Experience with reinforcement learning or alignment methods
  • Experience working with longitudinal or structured data at scale
  • Experience working with clinical, biomedical, or scientific domains

Compensation

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

Skills

PyTorchMachine LearningDeep LearningReinforcement LearningLLMsNLPSequence ModelingLongitudinal DataFoundation Models

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