Build and improve AI systems for clinical products as a Senior Machine Learning Engineer in a hybrid role at Ambience Healthcare.
Posted by employer 2 months ago
First seen on Joblaze 1 month ago
Last verified on the company career page 1 day ago
Role intensity
70% hands-on coding
AI in the day-to-day
You’ll work closely with clinicians, product managers, and fellow engineers to translate cutting-edge research into reliable, production-grade AI systems.
Requirements
Not disclosed in this posting: visa sponsorship.
Benefits
Joblaze summary
In the role of Senior Machine Learning Engineer at Ambience Healthcare, the individual will focus on developing and refining AI systems that enhance clinical products, taking ownership of complex projects from inception to deployment. Key skills include expertise in production AI, particularly with LLMs and agentic systems, as well as proficiency in Python and modern ML frameworks. This position is ideal for seasoned professionals with a strong background in applied AI and a collaborative mindset, particularly those who thrive in high-stakes environments like healthcare.
Joblaze insights
Quick facts
From the original posting
As a Senior Machine Learning Engineer at Ambience, you will build and improve the AI systems that power our clinical products. You’ll own complex projects end-to-end, from diagnosing production failures and designing evaluations to building, deploying, and iterating on model and agentic systems.
This is a highly hands-on role with significant technical ownership. You’ll work closely with clinicians, product managers, and fellow engineers to translate cutting-edge research into reliable, production-grade AI systems.
Our engineering roles are hybrid — working onsite at our San Francisco office three days per week.
Build Trustworthy AI Evaluation Systems: Design and own evaluation pipelines for LLM and agentic systems, combining automated graders, regression testing, production feedback, and human evaluation to measure real product quality.
Improve Production Model Behavior: Diagnose high-impact failure modes and test improvements across prompting, retrieval, context, routing, data, fine-tuning, or other model and system interventions.
Build Agentic AI Systems: Develop production systems involving tool use, retrieval, context and state management, routing, orchestration, tracing, and failure recovery.
Build Data and Improvement Flywheels: Turn production failures and user feedback into better datasets, evaluations, and model behavior through active learning and systematic iteration.
Stay at the Cutting Edge: Distill insights from recent research in LLMs, agents, NLP, speech, and multimodal AI and translate promising ideas into practical experiments.
Own AI Systems End-to-End: Work across models, data, evaluation, orchestration, serving, and observability, while remaining deeply hands-on in code and production debugging.
Strong Production AI Experience
5+ years in production ML, research engineering, or applied AI.
Have built a consequential production AI system or materially improved model behavior in production.
Strong understanding of modern LLMs, transformers, and production AI systems.
Deep Evaluation Experience
Experienced designing evaluations for LLMs, agents, or other complex AI systems.
Can turn ambiguous quality problems into measurable dimensions, datasets, and experiments.
Familiar with challenges such as grader bias, leakage, misleading aggregate metrics, regression detection, and offline-online mismatch.
Agentic Systems Experience
Experience building production systems involving multiple models, tools, retrieval, context, state, routing, or orchestration.
Understands reliability and failure modes in complex AI workflows, not just individual model calls.
Production-Grade Software Engineer
Proficient in Python and modern ML frameworks; PyTorch preferred.
Comfortable with deployment, observability, CI/CD, and containerized systems.
Still highly hands-on: writes code, inspects traces, analyzes failures, and debugs production systems.
Data-Centric AI Developer
Skilled at building high-quality datasets and feedback loops.
Experienced using production failures, user feedback, and active learning to improve model and system quality.
Effective Interdisciplinary Collaborator
Able to work closely with clinicians, product managers, and fellow engineers.
Strong communicator who can simplify complex AI concepts for diverse audiences.
Comfortable owning ambiguous technical problems and driving them to measurable outcomes.
Nice-to-Haves
Experience with realtime voice, conversational AI, or multimodal systems.
Experience with fine-tuning, post-training, or model adaptation.
Prior work in healthcare, clinical AI, or other regulated, high-stakes industries.
Experience interviewing or mentoring ML engineers.
Open-source contributions to ML, agent, or evaluation tooling.
Work on mission-critical AI technology that directly improves clinicians’ day-to-day lives and health system financial health across some of the most complex, high-stakes workflows in the world.
Comprehensive medical, dental, and vision coverage for you and your dependents
Parental leave to support your family needs
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