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Lila Sciences

Software Engineer, AI Platform

Cambridge, MA USA$148k–$210kfull-timemidAdded today

About this role

Join Lila Sciences as a Software Engineer to build high-performance ML libraries and infrastructure supporting machine learning research and workflows. You'll architect scalable systems, design CI/CD pipelines, and maintain distributed computing environments alongside ML researchers in a fast-paced scientific AI company.

What you'll do

  • Design and develop high-performance ML libraries implementing researcher-designed algorithms
  • Build CI/CD pipelines and integration tests for machine learning workflows
  • Architect repository structures adhering to consistent engineering standards
  • Debug and maintain Ray-based distributed compute environments
  • Create data ingestion pipelines connecting lab data to ML teams
  • Support MLOps infrastructure and DevOps tooling

What they're looking for

  • Python or Go (8+ years commercial experience)
  • Kubernetes and containerization
  • Infrastructure-as-code tools
  • MLOps systems and GitOps (ArgoCD, Github Actions)
  • Orchestration frameworks (Ray, Argo, Airflow)
  • CI/CD pipeline design
  • Monitoring and logging tools (Prometheus, Grafana)
  • Distributed systems and scalable architecture

Benefits

  • Comprehensive medical, dental, and vision coverage
  • Employer-paid life and disability insurance
  • Flexible time off with generous holidays
  • Paid parental leave
  • Educational assistance program
  • Company-subsidized lunch and commuter benefits
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Lila Sciences

Lila Sciences builds AI and automation systems for scientific research, including tools for automated analysis, control systems for lab operations, and large language models for scientific tasks. The company is hiring software engineers, machine learning scientists, research engineers, and automation specialists to develop and maintain these scientific computing platforms.

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Likely interview questions

  • Describe your experience building and maintaining ML libraries or MLOps infrastructure at scale.
  • How have you optimized CI/CD pipelines for machine learning workflows?
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