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Profluent

Software Engineer, Machine Learning Platform

Emeryville, California, United States; Hybrid (2-3 days on-site)From $330kmidAdded 6 days ago

About this role

Profluent, an AI lab for biology, is seeking a Software Engineer for its ML Platform team to build infrastructure and tools supporting both model training and protein design workflows. You'll work on distributed systems, inference stacks, and research tooling while collaborating closely with ML scientists and design teams in a hybrid role based in Emeryville, CA.

What you'll do

  • Build and maintain infrastructure for model training, inference, and protein design workflows
  • Develop research tooling and platforms that standardize workflows for ML scientists
  • Translate protein design processes into reproducible, scalable workflows
  • Optimize training and inference workloads to reduce compute and storage costs
  • Implement CI/CD, monitoring, and alerting for services and applications
  • Identify and implement engineering best practices across the team

What they're looking for

  • Backend or full-stack engineering (5+ years)
  • Building tools and platforms for technical users
  • High-performance systems design
  • Python and backend frameworks
  • PyTorch and MLflow (preferred)
  • Distributed systems and job orchestration
  • Inference optimization and model serving
  • CI/CD and infrastructure automation

Benefits

  • Competitive compensation with equity participation
  • 401(k) with strong employer match
  • Comprehensive health, dental, and vision insurance
  • Generous PTO and work-life balance commitment
  • Professional development in AI and biology
  • Meaningful impact on protein design innovation
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Profluent

Profluent develops AI-powered protein design and biomolecular engineering solutions, leveraging machine learning and generative modeling to advance biomedical applications. The company is hiring Machine Learning Scientists specializing in reinforcement learning and generative models, as well as Sustaining Engineers to maintain and enhance their automated high-throughput operations infrastructure.

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Likely interview questions

  • Describe a complex infrastructure system you've built for technical users—what made it successful and how did you measure adoption?
  • Tell us about your experience transitioning research ideas or prototypes into production systems. What were the biggest challenges?
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