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Machine Learning Engineer - Agentic Systems
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What they do
A Machine Learning Engineer specializes in designing, building, and deploying machine learning models. They utilize statistical and mathematical techniques, parallelizing processing, hyperparameter tuning, and other optimization methodologies to improve model performance. Responsibilities also include collecting and preprocessing large datasets, conducting exploratory data analysis, working closely with data engineers to understand data requirements, and engineer input variables for machine learning models.
$168,439 / year median in California
Job Description
Machine Learning Engineer - Agentic Systems at Lind Machine Learning Engineer - Agentic Systems at Lind in Millbrae, California Posted in about 16 hours ago.
Type:
full-time Who We Are 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. About the Role 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. Qualifications 2+ Years of LLM Agentic harness development Strong 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.