Machine Learning Engineer

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

You Might Be a Fit If You…

• Have a strong foundation in ML research or systems—through an advanced degree or high-impact work in industry

• Have deep experience with NLP and LLMs (e.g., fine-tuning, LoRA, RAG, quantization) and are comfortable with frameworks like PyTorch, Hugging Face, and LangChain

• Have shipped ML models in production environments—especially where latency, safety, or interpretability were critical

• Are excited by ambiguous, zero-to-one problems and can think creatively about tradeoffs between performance, explainability, and reliability

• Thrive in fast-moving, ambiguous, enjoy working on open-ended technical challenges, and work well with minimal oversight

• (Bonus) Have published ML research or contributed to the broader ML community

• (Bonus) Have worked with clinical, biomedical, or claims data—or are excited to learn the domain deeply

Compensation

The expected Pay Scale is $165,000-250,000 annually, in addition to equity and comprehensive benefits. Compensation packages are highly variable based on a variety of factors including experience and expertise. If your compensation expectations fall outside this range, we still encourage you to apply.

Why You Should Join Us

• Backed by top investors: General Catalyst, Conviction, and YC

• Tight-knit, world-class team with a deep sense of mission

• Huge greenfield opportunity with significant ownership and room for growth

• Competitive salary and equity compensation. The equity upside of an early-stage startup with the product-market fit of a later-stage company.

• Excellent benefits and versatile health, dental, and vision coverage plans

• Paid parental leave

• Lunch and dinner provided at the office

• Unlimited PTO