What you'll be doing:

• Model Training & Deployment: Own the end-to-end lifecycle of production models, from training and fine-tuning (SFT, RLHF) to deployment, monitoring, and iterative updates in a live clinical environment;
• Production Model Maintenance: Monitor model performance post-launch, identify degradation or drift, and implement updates to keep models aligned with clinical requirements and evolving product needs;
• Engineering Excellence: Champion and establish software engineering best practices within the research team, including code quality, testing, reproducibility, and documentation, elevating the team’s production readiness;
• Bridge Research & Engineering: Act as the primary liaison between the AI Research and Engineering teams, ensuring research breakthroughs are translated into scalable, reliable product features;
• Data & Evaluation Pipelines: Build and maintain robust data pipelines and evaluation frameworks that power model training, testing, and continuous improvement.

What you need to have:

• PhD or MSc in a STEM field (preference for PhD in Computer Science);
• Research & Engineering Blend: 4+ years of professional experience spanning both ML research and engineering, with a demonstrated ability to take models from experimentation to production;
• Model Training Expertise: Hands-on experience with LLM fine-tuning, post-training techniques (SFT, RLHF), and model evaluation in production settings;
• Proficiency in Python and SQL (TypeScript is a plus) alongside modern software development best practices, including version control, testing, and CI/CD;
• Proven ability to introduce and uphold engineering standards within research-oriented teams, mentoring researchers on production best practices;
• Comfort with Ambiguity: The ability to thrive in the fast-moving chaos of a newly launched product, iterating rapidly on models and infrastructure that are not yet stable, while maintaining the highest quality and safety standards throughout;
• The ability to translate complex research concepts for engineering teams and vice versa, ensuring alignment across both functions ensuring alignment across both functions.