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Lind is seeking a Machine Learning Engineer — Agentic Systems in San Mateo, CA for a full-time hybrid role. You will build and optimize agent harnesses, develop models, and collaborate with product and clinical teams to translate requirements into robust ML solutions.
Ideal candidates have 2+ years in LLM harness development, strong CS fundamentals, and experience with healthcare data pipelines. You will work with large datasets, run experiments, monitor performance, and contribute to production
We'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.
The 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.