Machine Learning Engineer

The Role

We’re looking for an ML Ops Engineer to own the infrastructure and systems that move machine learning models from research into reliable, observable, production-grade clinical workflows.

This role sits at the intersection of deep learning systems, infrastructure, and production engineering
. You will partner closely with research, backend, and product teams to ensure models are deployable, scalable, measurable, and correct in real-world environments.

This is a hands-on role with ownership across training pipelines, inference systems, monitoring, and iteration loops
.

• Deploy, operate, and optimize GPU-based inference systems for low-latency, high-throughput workloads.
• Own model serving infrastructure, including batching, caching, and runtime optimization
  .
• Implement and maintain APIs for real-time model inference
  .

• Design and maintain CI/CD pipelines for model training, testing, validation, and rollout.
• Build reproducible experimentation frameworks for training, tuning, and deployment cycles.
• Manage distributed training and inference infrastructure
  , including GPU scheduling and scaling.

• Profile and benchmark models in production, identifying bottlenecks in latency, memory, and throughput
  .
• Design observability systems to track model performance, drift, failures, and uptime
  .
• Use production signals to drive iteration decisions and system-level improvements
  .

• Partner with research teams to transition models from research to production systems.
• Collaborate with product engineers and clinicians to meet real-world workflow constraints
  .
• Make clear, defensible tradeoffs between model quality, system cost, and operational reliability
  .

• 4+ years of experience in ML Ops, infrastructure, or distributed systems
  .
• Strong hands-on experience deploying and operating GPU-based inference systems
  .
• Deep familiarity with Py Torch , including performance tuning and debugging.
• Proven ability to own systems end-to-end and operate independently in ambiguous environments.

• Experience optimizing LLM or deep learning inference (batching, caching, memory efficiency).
• Comfort reasoning about distributed systems tradeoffs (compute, communication, scaling).
• Clear ownership of production systems—not just research exposure.

• Familiarity with DICOM, HL7
  , or healthcare data standards.
• Experience working in regulated or safety-critical ML environments
  .
• Experience with Docker, Kubernetes
  , and cloud environments (AWS or GCP).

We hire for clarity, ownership, and judgment.

The ideal engineer:

• Thinks in systems. Sees beyond individual tasks to how everything connects.
• Executes with precision. Moves quickly without sacrificing long-term quality.
• Owns outcomes. Takes responsibility across design, build, and delivery.
• Builds with purpose. Writes code that improves lives, not just benchmarks.

You’ll work directly with leading engineers, clinicians, and researchers from UC Berkeley and UCSF — building products that didn’t exist before. If you want to shape how AI enters the clinic, and you care about craft as much as impact, this is your team.