Machine Learning Engineer

About Latent

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

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