
As a Machine Learning Engineer at Latent, you’ll design and deploy advanced models at the frontier of medical language understanding. You will develop systems that can interpret long-form clinical text, generate auditable justifications for medical decisions, and reason over structured and unstructured data to automate the prior authorization process end-to-end.
You’ll work on some of the most pressing problems in applied AI—balancing model expressiveness with verifiability, maintaining safety in open-ended generation, and scaling LLMs to production in high-stakes environments. This is a rare opportunity to bring research into production at the edge of what’s possible in medicine and AI.
This is a high-impact, high-ownership role based full-time onsite in our San Francisco office.
What You’ll Do
Train and fine-tune large open-source language models for clinical reasoning, medical question answering, and evidence-grounded generation, where the stakes are human health
Design and scale multimodal embeddings to encode clinical documents, structured EHRs, and payer policies in a unified space
Own the lifecycle of ML systems—from research prototypes to fault-tolerant, privacy-compliant services running in production
Build robust retrieval pipelines for real-time semantic search and RAG architectures in the clinical domain
Collaborate with clinicians, engineers, and product leaders to ensure outputs are interpretable, auditable, and aligned with real-world constraints
Contribute to a culture of ML excellence through code reviews, experimentation frameworks, and internal knowledge sharing
Take the next step in your career journey