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.
About the job
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
PyTorch, Machine Learning, Deep Learning, Reinforcement Learning, LLMs, NLP, Sequence Modeling, Longitudinal Data, Foundation Models
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