Staff ML Engineer, Clinical AI Frontiers

ambiencehealthcare; San Francisco (CA); Hybrid; USD 250,000 - 350,000; Full time

Benefits offered by this job

Comprehensive medical, dental, and vision coverage

401(k) with company match

Flexible time off with no annual cap

Job summary

Ambience Healthcare is seeking a Staff ML Engineer for their Frontier AI team, responsible for addressing complex model quality challenges in clinical AI products. This hybrid role requires deep expertise in reinforcement learning, successful model deployment, and a passion for improving healthcare.

Located in San Francisco, you will contribute to innovative solutions that enhance clinician workflows and patient outcomes, fostering a culture of feedback and growth with competitive compensation and comprehensive benefits.

Qualifications

• Deep expertise in reinforcement learning and deep learning, developed in industry or a research setting.
• Strong track record of shipping model improvements in production.
• Publications at top‑tier venues are a strong plus.
• Comfortable spanning research and engineering.

Responsibilities

• Own foundational model research across clinical AI products.
• Build compounding learning loops from real‑world signals.
• Drive better grounding and reasoning in clinical questions.
• Push latency, accuracy, and cost optimizations.

Skills

Deep RL and Deep Learning Expertise

End‑to‑End Ownership

Mission‑Aligned

Strong Python fundamentals

Education

5+ years of ML engineering or applied research experience

Tools

PyTorch

Job description

Ambience Healthcare is seeking a Staff ML Engineer for their Frontier AI team, responsible for addressing complex model quality challenges in clinical AI products. This hybrid role requires deep expertise in reinforcement learning, successful model deployment, and a passion for improving healthcare.

Located in San Francisco, you will contribute to innovative solutions that enhance clinician workflows and patient outcomes, fostering a culture of feedback and growth with competitive compensation and comprehensive benefits.