Senior ML Engineer, Agentic AI

Ellipsis Health•San Francisco, CA

1d•$160,000 - $210,000

About The Position

Ellipsis Health is creating AI/ML products to address healthcare staffing challenges and administrative burdens using conversational AI and patented voice biomarker technology. The Senior ML Engineer, Agentic AI will be a core contributor and technical leader within the AI Research & Engineering team, working between scientific research and production-grade systems. The role drives the conceptualization, architecture, and deployment of frontier agentic systems, collaborating with infrastructure and platform teams to translate advances in LLMs and agentic AI into robust, secure, low-latency clinical applications.

Requirements

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

Nice To Haves

Master’s degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative technical field.
At least 8 years of industry experience specializing in advanced data structures, algorithms, distributed systems, and scalable ML infrastructure.
At least 3 years of technical leadership experience, including leading complex technical project workstreams, defining engineering direction, or mentoring technical talent.
Experience in complex, collaborative, matrixed organizations and highly regulated technical integrations, such as healthcare, fintech, or aerospace.
Contributions or publications at top-tier machine learning venues such as NeurIPS, ICML, or KDD, focused on agentic frameworks, reinforcement learning, or data benchmarks.

Responsibilities

Act as the technical authority for the Agentic AI roadmap, guiding engineering pods from research prototyping through production scale.
Design, build, and optimize scalable agentic systems and orchestration pipelines supporting multi-step reasoning, dynamic memory allocation, long-context evaluation, and tool utilization.
Translate frontier machine learning research into enterprise software, implementing and adapting post-training fine-tuning, preference alignment, and automated prompt optimization techniques.
Architect and maintain quantitative evaluation frameworks and continuous testing infrastructure, including LLM-as-a-judge rubrics and statistical tracking to evaluate agent capabilities and prevent product regressions.
Develop and scale automated data curation pipelines that turn user feedback, execution logs, and expert labels into high-quality training datasets for model fine-tuning.
Collaborate with clinical experts, product managers, and software engineers to ensure AI platforms operate safely and ground outputs in validated healthcare protocols.
Set coding standards, lead design reviews, standardize tooling, and mentor ML and software engineers.