Machine Learning Engineer

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Latent is building the intelligence infrastructure for American healthcare. Our products are already helping hospitals and clinics dramatically increase workflow output, speed up patient access to medications, and boost provider revenue. Our flagship multi‑modal search and question‑answering platform analyzes EHR data to surface the most relevant information, reducing operational overhead and improving care delivery.

We’re a small, mission‑driven team backed by General Catalyst, Conviction, and YC, tackling some of healthcare’s hardest technical challenges. If you’re passionate about applying cutting‑edge machine learning in a high‑stakes domain, we’d love to meet you.

About The Role

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

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