Machine Learning Engineer
$225k - $300kWe Love AI Jobs
- Machine Learning EngineerLatentSan FranciscoFull-time$225,000 – $300,000 / yearAI tools:PyTorchUpload your resume once and Scout applies for youApply with ScoutShare this job:LinkedInXSave imageYou’ll own production machine-learning systems that support clinical workflows, from problem definition and modeling through deployment and monitoring. The work includes training and evaluating language models for clinical reasoning and evidence-grounded responses, with decisions that can affect patient outcomes.* Permanent* On-site* Senior / Staff### Skills and expertisePyTorchlarge language modelsreinforcement learningmodel fine-tuningmodel evaluationproduction ML systemsdata pipelinesclinical data### Key responsibilities* Own end-to-end ML systems, from architecture and modeling through production infrastructure.* Train and fine-tune language models for clinical reasoning and evidence-grounded generation.* Build safety and clinical-validity evaluations, then monitor systems in production.## How this job comparesCompared with 410 open senior machine learning jobs on October 10, 2026.Pay: This job pays $225K to $300K a year. The 143 open senior machine learning jobs that publish US pay start at $186K on average, so this job starts about 21% higher.Similar open jobs: We Love AI Jobs lists 410 open senior machine learning jobs at 176 companies.Remote work: This job is on site. 90 of the 410 senior machine learning jobs (22%) are remote.AI in this role: High. AI is the core of the work: building, training or deploying AI systems. The core work is building, training, fine-tuning, and evaluating language models for clinical workflows. The posting specifically calls out production ML systems and model safety evaluation.## Full descriptionAbout LatentLatent 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 RoleThe 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 cliniciansWhat 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 datasetsWhy 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
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