Staff Machine Learning Engineer at Ambience

Department: Engineering

Compensation

$250,000 – $350,000/yr

Setup: Hybrid
Location: San Francisco, California
Type: Full-time
Level: staff+
Posted: Mar 17, 2026

Description

Ambience is hiring a Staff Machine Learning Engineer to set technical direction for production AI systems powering clinical products. The role owns AI quality and evaluation, production model improvement, post-training capabilities, agentic systems, self-improving feedback loops, and the 12-month technical roadmap, while leading technical reviews and mentoring engineers. It requires 7+ years of production machine learning, research engineering, or applied AI experience, deep expertise with LLMs and transformers, and proficiency in Python and modern machine learning frameworks. The role is hybrid, with onsite work at the San Francisco office three days per week, and offers a base compensation range of $250,000–$350,000 per year, exclusive of equity.

Required Skills

LLMs
Transformers
Python
PyTorch
MLOps
CI/CD
Observability
Containerized deployments
Agent systems
Supervised fine-tuning
Preference optimization
Reinforcement learning
Distillation
Synthetic data

Additional Information

Required Previous Experiences: 7+ years of production machine learning, research engineering, or applied AI experience
Leading or materially shaping consequential AI systems with real production impact
Designing evaluations for LLMs, agents, or other complex AI systems
Building or architecting production systems involving multiple models, tools, retrieval, context and state management, routing, orchestration, tracing, and failure recovery
Improving model behavior through post-training or model adaptation techniques
Proficiency in Python and modern machine learning frameworks
Experience with modern MLOps, CI/CD, observability, and containerized deployments
Building large, high-quality datasets and feedback loops
Using production failures, user feedback, and active learning to improve model and system quality
Designing data and evaluation infrastructure across multiple teams
Preferred Qualifications: Experience with real-time voice, conversational AI, or multimodal systems
Prior work in healthcare, clinical AI, or other regulated, high-stakes industries
Experience interviewing or hiring machine learning engineers
Open-source contributions to machine learning, agent, evaluation, or post-training tooling