AI / Machine Learning Talent

Ensemble Health Partners•San Jose, CA

1d•Hybrid

About The Position

Ensemble Health Partners is the leading Revenue Cycle Management (RCM) partner to U.S. health systems. We sit between hospitals, payers, and patients, and we are responsible for billions of dollars of healthcare revenue moving accurately and on time. AI is increasingly central to how we do that work — from predicting denials and automating coding to flagging payment integrity issues and routing accounts to the right action at the right time. These roles exist where those AI systems meet the business outcomes we are paid to deliver. We are continuously building a pipeline of exceptional AI/ML talent across research, data science, and engineering. This general application allows candidates to be considered for multiple roles across the AI Lab. Final role alignment (e.g., Data Scientist, AI Research Scientist, Machine Learning Engineer) and leveling will be determined based on interview performance, experience, and team needs.

Requirements

Proven experience working with machine learning or AI systems in real-world or production settings
Strong problem-solving skills and ability to operate in ambiguous, evolving environments
Ability to connect technical work to measurable business or product outcomes
Experience collaborating across teams (engineering, product, data, or research)
Ownership mindset—taking work from idea through execution and impact
Programming: Python (required), plus familiarity with SQL and other languages (e.g., Java, C++, Go)
Machine Learning: Model development, evaluation, and optimization
Deep Learning: Frameworks such as PyTorch, TensorFlow, Hugging Face
LLMs & Modern AI: Fine-tuning, prompt engineering, evaluation frameworks, or emerging techniques (e.g., PEFT, RLHF)
Data & Analytics: Statistics, experimentation, causal inference, and working with real-world datasets
Systems & Infrastructure (Engineer-leaning candidates): Model serving, APIs, and distributed systems
Training at scale (e.g., multi-GPU / distributed training)
Performance optimization and cost efficiency
Research (Research-leaning candidates): Experiment design, evaluation rigor
Bachelor’s degree in Computer Science, Engineering, Data Science, Mathematics, or related field required
Relevant industry experience typically ranges from 3+ years (Senior) to advanced leadership/ownership experience (Staff/Principal)

Nice To Haves

Publication or novel contributions

Responsibilities

Design, build, evaluate, and improve machine learning and AI systems in production environments
Translate business problems into AI/ML solutions and measurable outcomes
Develop and run experiments to evaluate models, including defining metrics, datasets, and success criteria
Productize models into scalable, reliable, and cost-efficient services
Optimize training and inference performance (latency, throughput, cost)
Analyze real-world system behavior and production data to drive continuous improvements
Partner cross-functionally with engineering, product, and domain experts to deliver end-to-end solutions
Contribute to AI research, experimentation, and adoption of new techniques where applicable
Communicate findings, tradeoffs, and recommendations clearly to both technical and non-technical stakeholders