Latent Defense
Machine Learning Engineer
About this role
Latent Health seeks a Machine Learning Engineer to design and deploy production ML systems that deliver personalized healthcare using clinical knowledge and patient history. You'll own end-to-end ML systems—from problem formulation through production—working on LLM training, model evaluation, and real-world clinical integration in a small, high-impact team.
What you'll do
- Own end-to-end ML systems including architecture, data pipelines, modeling, evaluation, and production infrastructure
- Train and fine-tune large language models for clinical reasoning, medical question answering, and evidence-grounded generation
- Develop evaluation frameworks to ensure model safety and clinical validity in high-stakes environments
- Make tradeoffs across accuracy, latency, cost, and safety for production deployment
- Integrate ML systems into product workflows and patient-facing applications
- Monitor and iterate on system performance based on real-world usage and clinical feedback
What they're looking for
- Machine learning and software engineering fundamentals
- PyTorch or similar deep learning frameworks
- Production ML systems design and deployment
- Large language model training and fine-tuning
- Model evaluation and safety assessment
- Problem formulation in ambiguous domains
- Independent work in high-ambiguity environments
- Product and engineering judgment
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Latent Defense
Latent Defense builds modern clinical AI systems designed to replace outdated EHRs and improve healthcare workflows for providers and patients. The company is hiring frontend engineers, DevOps infrastructure specialists, backend developers, and machine learning engineers to develop intuitive interfaces, ensure production reliability, power clinical data pipelines, and deploy personalized healthcare ML systems.
View all jobs at Latent DefenseLikely interview questions
- Walk us through a production ML system you owned end-to-end. How did you handle the tradeoff between accuracy, latency, cost, and safety?
- Describe your experience fine-tuning or training large language models. What framework did you use, and how did you evaluate whether the model was ready for production?