Machine Learning Engineer — Agentic Systems

Who We AreWe're a team of engineers, data scientists, clinical research professionals, and clinicians who believe every patient deserves access to the best possible treatment options,  no matter who they are, where they live, or where they receive their care.We help large health systems become better at research. Our AI-powered platform makes sense of complex clinical trial criteria and every patient's medical record. It puts that intelligence in the hands of principal investigators, research staff and care teams, so they can identify eligible patients faster and bring research directly to where patients already are. That same intelligence layer supports research administrators and leadership managing studies across sites.  The result: PIs run more efficient, higher-enrolling studies. Research administrators gain visibility and control across a growing, distributed portfolio. Clinicians and caregivers get transparent, timely insights to guide treatment decisions. And more patients, regardless of zip code, get access to the trial that might be right for them. We're breaking down the barriers between patients and the research that could change their care.About the RoleThe Machine Learning Engineer — Agentic Systems role is a full-time, hybrid position based in San Mateo, CA, with flexibility for work from home. Day-to-day responsibilities include building and optimizing agent harnesses, model development and collaborating with product and clinical teams to translate requirements into robust ML solutions. The role also involves working with large, heterogeneous datasets, conducting experiments, monitoring performance, and iteratively improving systems for speed, scalability, and safety. The engineer will participate in code reviews, documentation, and deployment processes to ensure reliable delivery of ML features into production.Qualifications2+ Years of LLM Agentic harness developmentStrong foundation in Computer Science, with proficiency in Algorithms and software engineering principles.Proficiency in modern ML tools and languages (e.g., Python, PyTorch/TensorFlow, SQL) and working with real-world data pipelines.Experience building and deploying production ML systems, preferably in healthcare, life sciences, or similarly regulated domains.Ability to collaborate with cross-functional teams and communicate technical concepts to non-technical stakeholders.