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Glimpse

Cloud and ML Infrastructure Engineer

Somerville (Remote)$120k–$190kfulltimemidAdded 1 month ago

About this role

Glimpse seeks a Cloud and ML Infrastructure Engineer to build scalable infrastructure for their battery quality management platform. You'll design and maintain production ML systems on AWS, manage GPU compute workflows, and implement compliance controls for a fast-growing climate tech startup.

What you'll do

  • Build and maintain production ML infrastructure on AWS, with focus on GPU compute workflows
  • Design monitoring and alerting systems for ML algorithms and edge computing pipelines
  • Implement automated compliance controls for SOC 2, NIST 800-171, and similar frameworks
  • Develop and optimize performance-critical Python applications for image processing and AI feature detection
  • Scale cloud infrastructure to support growing battery quality analysis workloads
  • Collaborate on MLOps workflows and training infrastructure

What they're looking for

  • AWS infrastructure and production ML/AI deployments
  • Python development, especially performance-sensitive applications
  • GPU compute and distributed training management
  • Monitoring and alerting system design
  • Compliance frameworks (SOC 2, NIST 800-171)
  • MLOps and infrastructure automation
  • Rust or high-performance languages (nice-to-have)
  • Edge computing systems

Benefits

  • Equity compensation alongside $120k–$190k salary
  • Growth opportunity at well-funded Series A climate tech company
  • Work on safety-critical technology for electric vehicle industry
  • On-site presence in Somerville with flexibility for exceptional candidates
  • Opportunity to work at intersection of hardware and software engineering
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Glimpse

Glimpse builds a battery quality management platform powered by cloud and ML infrastructure to support climate tech applications. The company is hiring infrastructure and engineering roles focused on scaling AWS-based systems, managing GPU compute workflows, and implementing compliance controls.

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Likely interview questions

  • Walk us through your experience managing GPU-compute workflows on AWS. What challenges have you encountered and how did you solve them?
  • Describe a time you designed monitoring and alerting systems for ML algorithms in production. How did you decide what metrics to track?
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