Senior Machine Learning Engineer

present of AI; San Francisco (CA); Sur place; USD 180 000 - 240 000; Plein temps

Résumé du poste

Ambience Healthcare in San Francisco is seeking a Senior AI & Data engineer to own AI systems end-to-end: detect failures in live products, design measurable quality tests, and ship fixes across models, data and orchestration.

A strong candidate has years of hands-on production ML work, has improved a real AI system in use, and can write robust code and debug live systems rather than only run experiments.

Qualifications

• Hands-on production ML experience in live systems.
• Ability to write robust production code and debug live ML workflows.
• Experience collaborating with clinicians and product teams to discuss tradeoffs.

Responsabilités

• Own AI systems end-to-end from detection to fixes across models, data and orchestration.
• Design and implement ways to measure quality and reliability in AI systems.
• Build and ship fixes and features based on evaluation results and real usage.

Connaissances

Python

PyTorch

Description du poste

AI & Data · San Francisco · FullTime · Senior level

The role asks for someone who can own AI systems from start to finish: spotting failures in live products, designing ways to measure quality, and then building and shipping fixes across models, data and orchestration. A strong candidate has several years of hands on production ML work, has already improved a real AI system in use, and is comfortable writing code and debugging live systems rather than only running experiments. The work also calls for close collaboration with clinicians and product people, so clear communication about technical tradeoffs matters as much as modeling skill.

How it fits the AI era

This is a build the AI role, not a role reshaped by it. The core of the job is making large language models and agent workflows reliable enough for clinical settings, which means the human work centers on judgment: deciding what quality looks like, turning vague problems into measurable tests, and tracing why a system failed. The tools and models will keep changing, but the discipline of evaluation, data curation and production debugging stays central.

How it builds AI experience

A person here would gain deep experience in evaluating and hardening LLM and agent systems for a high stakes clinical product, including building graders, regression tests and feedback loops from real usage. They would also learn how speech, retrieval and tool use combine in a live documentation workflow used by health systems. To prepare, building and shipping an agent or LLM feature with a real evaluation setup and studying failure modes in multi step systems would be the most direct path.

Worth showing

• Production LLM systems
• Agentic orchestration
• Python and PyTorch

This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.

About Ambience Healthcare

AI that listens to clinical visits and drafts the documentation, coding and clinical checks so doctors spend less time on the screen.