Senior Machine Learning Engineer - Frontier AI / Clinical Intelligence

About

Join an innovative AI healthcare startup focused on revolutionizing clinical workflows with cutting-edge AI technology. Our platform enhances real-time documentation, coding, and decision support across major health systems, operating in complex environments that require high accuracy and low latency.

As a key member of our team, you'll tackle challenging model quality problems and lead research that has a direct impact on improving clinical outcomes.

Responsibilities

• Develop and refine advanced clinical AI models for coding, scribing, and chart understanding.
• Create learning loops from real-world clinician feedback and data audits.
• Implement long-context reasoning across patient health records for improved clinical insights.
• Design and enhance retrieval, grounding, and clinical quality assurance systems.
• Optimize model performance in terms of latency, cost, and overall effectiveness.

What You'll Achieve

• Lead research and architectural decisions to advance machine learning capabilities.
• Identify issues and implement comprehensive end-to-end improvements.
• Develop robust systems that continuously leverage real-world data for enhancement.
• Utilize advanced techniques such as Reinforcement Learning with Human Feedback (RLHF), model distillation, and optimization strategies.
• Collaborate closely with engineering, product, and domain experts to align goals and drive impact.

What We Seek

• A minimum of 5 years of experience in machine learning engineering or applied research.
• Deep expertise in reinforcement learning and deep learning methodologies.
• Proven experience in transitioning models from research to production.
• Strong programming skills in Python with extensive knowledge of PyTorch.
• A track record of enhancing model performance in real-world production environments.

Preferred Qualifications

• Publications in top-tier machine learning journals or conferences.
• Experience working with clinical or regulated datasets.
• Background in retrieval-augmented generation, long-context models, or advanced reasoning systems.

Role Details

• Hybrid work structure with three days onsite in San Francisco.
• Compensation package ranges from $250K to $350K base salary plus equity.