Founding AI/Machine Learning Engineer

What You Will Bring

• 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.).

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

What You’ll Work On

• 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.

Bonus Points

• Research Track Record: Published research in high-impact journals or top-tier ML/AI conferences (NeurIPS, ICML, ICLR, CVPR, ACL).
• Top Lab Experience: Background working or interning at top research labs (e.g., FAIR, DeepMind, OpenAI, Google DM, MSR, Stanford/CMU/MIT labs).
• Domain Expertise: Experience dealing with multimodal health data, clinical reasoning, or safety-critical ML systems.
• Entrepreneurial Spirit: You have founded a company, built early-stage products, or enjoy the "zero-to-one" phase of building.