Post-Training & Reasoning AI Engineer — Hybrid (Equity)

Cobalt; San Francisco (CA); Hybrid; USD 150,000 - 230,000; Full time

Benefits offered by this job

Founding-team equity

Salary discussion

Job summary

Cobalt is seeking a Research Engineer to advance post-training optimization for expert reasoning. This role welcomes candidates pursuing a PhD or Master’s and involves designing SFT, DPO, and RL experiments with cross-functional teams.

You will work with healthcare domain data to improve AI performance and interpretability, publish where appropriate, and engage directly with frontier labs and healthcare AI partners.

Qualifications

• Strong ML engineering fundamentals.
• Real exposure to post-training methods (SFT, preference optimization, RL fine-tuning).
• A track record of shipping research or research-grade engineering.
• Comfortable working with a part-time research lead.
• Excited by applied work in a domain with real-world consequences (healthcare).

Responsibilities

• Designing and running SFT, DPO, and RL experiments on reasoning traces.
• Building benchmarks and evals for clinical and adjudication reasoning.
• Turning raw expert outputs into high-quality training data and pipelines.
• Collaborating with frontier labs and healthcare AI partners on bespoke data/eval engagements.
• Publishing results where it makes sense.

Skills

ML engineering

Post-training methods

Research contributions

Team collaboration

Healthcare impact interest

Education

Pursuing PhD or Master’s

Tools

PyTorch

HuggingFace

vLLM

Deepspeed/FSDP

Job description

Cobalt is seeking a Research Engineer to advance post-training optimization for expert reasoning. This role welcomes candidates pursuing a PhD or Master’s and involves designing SFT, DPO, and RL experiments with cross-functional teams.

You will work with healthcare domain data to improve AI performance and interpretability, publish where appropriate, and engage directly with frontier labs and healthcare AI partners.