Machine Learning Engineer
Latent · San Francisco
About Latent
Latent is the enterprise pharmacy intelligence platform, powered by a clinical agentic engine, that enables health systems to grow their pharmacy operations and serve more patients. One engine works across the pharmacy journey to identify patients who qualify for care, improve medication access, capture eligible prescriptions and support patients throughout treatment. Latent’s platform enables more than 60 leading health systems that collectively serve an estimated 70 million people. Founded by Sriram “Sri” Somasundaram and Rishabh Jain, Latent has raised $80 million from Transformation Capital, Spark Capital, Conviction, General Catalyst, Y Combinator, and others.
The Role
The Machine Learning team is responsible for building systems that run in real clinical workflows. We work on verifiable reinforcement learning at scale, mid-training and post-training of foundation models, and novel objectives derived from longitudinal patient data. We are a small group of researchers and engineers focused on pushing the frontier while shipping real systems into production, and we expect engineers to take ownership of critical systems, not components.
As a Machine Learning Engineer, you will own the design, development, and operation of production-grade ML systems that run in real clinical workflows. You will drive systems from ambiguous problem definition through to reliable production deployment, setting technical direction along the way. We are primarily hiring for senior and staff-level engineers who are comfortable owning critical systems end-to-end. This role involves owning systems that directly impact real patient outcomes.
What You'll Do
Own end-to-end ML systems, including architecture, data, modeling, evaluation, and production infrastructure
Train and fine-tune large language models (LLMs) for:
Clinical reasoning
Medical question answering
Evidence-grounded generation
Make and own tradeoffs across accuracy, latency, cost, and safety in high-stakes production environments
Develop evaluation frameworks to ensure model safety and clinical validity
Integrate ML systems into product workflows and patient-facing applications
Monitor system performance in production and iterate based on real-world usage and feedback
Define what “correct” means in ambiguous clinical workflows in collaboration with engineers and clinicians
What We're Looking For
Strong foundation in machine learning and software engineering
Track record of building and owning ML systems in production where performance, reliability, or correctness materially mattered
Experience driving ambiguous ML problems from 0→1, including problem formulation, model design, and productionization
Hands-on experience with PyTorch or similar frameworks
Ability to operate independently in high-ambiguity environments with minimal guidance
Strong product and engineering judgment — you know when to use ML, when not to, and how to scope problems accordingly
Comfort working in a fast-moving, early-stage environment
Experience working on systems where decisions have real-world consequences (e.g., healthcare, finance, infrastructure)
Nice to Have
Experience deploying LLMs in production environments
Experience building distributed systems or large-scale data pipelines
Experience working with clinical, biomedical, or other regulated datasets
Why Join Latent
Backed by top-tier investors including General Catalyst, Conviction, and Y Combinator
Work alongside a high-caliber team building products our healthcare partners love
Work on mission-critical problems at the intersection of AI and healthcare
Real ownership and visibility
High-impact role on a small, fast-growing team
Benefits
Competitive compensation, including meaningful equity
Medical, dental, and vision insurance for employee and dependents
Flexible PTO policy
Paid parental leave
Fertility and family-building stipend through Carrot
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