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Machine Learning Engineer

Machine Learning Engineer

LatentSan Francisco, CA, United States
30+ days ago
Job type
  • Full-time
Job description

Join to apply for the Machine Learning Engineer role at Latent

Latent is building the intelligence infrastructure for American healthcare. 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

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, 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
  • Get notified about new Machine Learning Engineer jobs in Buffalo-Niagara Falls Area .

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    Machine Learning Engineer • San Francisco, CA, United States

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