Founding AI/Machine Learning Engineer

Deep Post-Training Expertise: Hands-on experience with post-training models for specific applications (SFT, RLHF, RLAIF, Reward Modeling, Knowledge Distillation, etc)

Strong Architectural Foundations: Deep understanding of Transformer architectures (attention mechanisms, positional encodings) and ML systems. And knowing when to use which model (sometimes it's simpler models that win!)

Large-Scale Training: Experience with distributed training frameworks and optimizing training jobs on GPU clusters.

High Velocity & Ownership: You thrive in ambiguous environments, learn quickly, and have a bias toward action. You are comfortable shipping in rapid cycles typical of early-stage startups.

Technical Stack: Proficiency in Python, PyTorch. Familiarity with the modern open-source LLM stacks (HuggingFace, Vertex, Vercel, etc.).

(Healthcare experience is preferred but not strictly required if you have exceptional ML fundamentals.)

Architect the Post-Training Stack: Lead the design and execution of alignment pipelines (SFT, RLHF, RLAIF) that bridge the gap between "exam-passing" models and "clinically useful" systems.

Leverage Proprietary Data: Utilize our proprietary and open source medical datasets to fine-tune models on edge cases that generic models miss.

Novel Technique Experimentation: Research and implement cutting-edge post-training methods to optimize model performance, aiming for improvements in calibration and reliability critical for healthcare.

Safety & Evaluation: Build rigorous evaluation frameworks (LLM-as-a-judge, benchmarks) to detect hallucinations, ensure clinical correctness, and guarantee safety before deployment.

Strategic Collaboration: Work directly with the co-founders to define the research roadmap and platform strategy.