Senior ML Engineer, Agentic AI

Ellipsis Health•San Francisco, CA

32d•$160,000 - $210,000•Onsite

About The Position

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.

Requirements

Bachelor’s degree in Computer Science, Machine Learning, Statistics, Engineering, or equivalent practical experience.
5+ years of practical software development experience with a heavy emphasis on product design, core system architecture, and shipping scalable software platforms (or 2+ years of post-PhD industry experience in a dedicated AI research/engineering role).
Advanced programming proficiency in Python and deep hands-on expertise with distributed machine learning or deep learning frameworks (e.g., PyTorch, JAX, or TensorFlow).
Proven track record of architecting, implementing, and deploying complex LLM-powered applications, multi-agent orchestrations, or autonomous systems in a production environment.

Nice To Haves

Master’s degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative technical field.
8+ years of industry experience specializing in advanced data structures, algorithms, distributed systems, and heavy-duty scalable ML infrastructure.
3+ years of technical leadership experience, including leading complex technical project workstreams, defining organizational engineering directions, or directly mentoring technical talent.
Experience working within complex, highly collaborative matrixed organizations on high-acuity, deeply regulated technical integrations (e.g., healthcare, fintech, or aerospace).
A solid track record of contributions or publications at top-tier machine learning venues (e.g., NeurIPS, ICML, KDD) with a specific focus on agentic frameworks, reinforcement learning, or data benchmark.

Responsibilities

Act as the technical authority for the Agentic AI roadmap, steering engineering pods from initial research prototyping through to production scale.
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.
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.
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.
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.
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.
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.