Research Scientist

The Role

As a Machine Learning Engineer, Research, you will own the design and development of novel modeling approaches that advance state-of-the-art clinical intelligence.

You will drive research from ambiguous problem definition through to validated results and downstream impact, shaping the technical direction of how models learn from longitudinal patient data.

We are primarily hiring for senior and staff-level engineers who are comfortable owning critical research problems end-to-end.

This role involves working on problems that directly impact real patient outcomes.

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

Benefits

• Competitive compensation, including meaningful equity
• Medical, dental, and vision insurance for employee and dependents
• Flexible PTO policy
• Paid parental leave
• Fertility and family-building stipend through Carrot