ML Engineer

Ellipsis Health is creating cutting-edge AI/ML products that solve healthcare staffing challenges and administrative burdens using conversational AI and our patented voice biomarker technology—helping deliver better healthcare for everyone. We are headquartered in Silicon Valley and are funded and supported by some of the most preeminent venture capital teams.

In this role, you will be a core contributor and technical leader within our AI Research & Engineering team. Operating at the sweet spot between cutting-edge scientific research and production-grade systems, you will drive the conceptualization, architecture, and deployment of frontier agentic systems. You will collaborate directly with core infrastructure and platform teams to translate algorithmic breakthroughs in LLMs and agentic AI into robust, secure, and low-latency clinical applications.

Responsibilities:

• Technical Leadership & Vision: Act as the technical authority for the Agentic AI roadmap, steering engineering pods from initial research prototyping through to production scale.

• Advanced Agentic Architecture: Design, build, and optimize scalable systems and orchestration pipelines capable of deep multi-step reasoning, dynamic memory allocation, long-context evaluation, and high-fidelity tool utilization.

• Research-to-Production Translation: Bridge the gap between frontier machine learning research and enterprise software design. Implement and adapt state-of-the-art techniques in post-training fine-tuning, preference alignment, and automated prompt optimization.

• Rigorous Evaluation & Benchmarking: Architect and maintain multi-dimensional quantitative evaluation frameworks and continuous testing infrastructure. Implement state-of-the-art LLM-as-a-judge rubrics, and statistical tracking to evaluate agent capabilities and guarantee zero-regression product updates.

• Data Mix Optimization: Develop and scale automated data curation pipelines to extract, filter, and structure user feedback signals, execution logs, and expert labels into ultra-high-quality training datasets for model fine-tuning.

• Cross-Functional & Clinical Collaboration: Work closely within a highly matrixed, collaborative organization alongside clinical experts, product managers, and software engineers to ensure AI platforms operate safely and ground outputs in validated healthcare protocols.

• Mentorship & Engineering Excellence: Foster a culture of technical rigor by setting high coding standards, leading design reviews, standardizing tooling, and mentoring other ML and software engineers across the team.