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STR

Machine Learning Engineer

Woburn, MA$115k–$140kfull-timemidAdded today

About this role

Join STR's Intelligence Division as a Machine Learning Engineer to develop and deploy AI/ML solutions for national security challenges. You'll work across diverse data types and platforms, translating research into production systems while collaborating with analysts and customers to deliver real-world impact.

What you'll do

  • Analyze large, complex datasets to identify patterns and extract actionable intelligence
  • Develop and implement AI/ML models from classical methods to frontier language models
  • Deploy models into production across cloud, on-premises, desktop, mobile, and edge environments
  • Build scalable backend services, APIs, and user interfaces for analytics platforms
  • Collaborate with researchers, engineers, and domain experts on multidisciplinary teams
  • Gather customer feedback and iterate on capabilities to meet mission requirements

What they're looking for

  • Python and data science stack (NumPy, Pandas, scikit-learn, PyTorch, matplotlib)
  • Machine learning model development and deployment
  • Data engineering and analytics pipeline construction
  • Git version control and collaboration tools (JIRA, Confluence)
  • Data visualization and dashboard creation
  • Big data frameworks (AWS, Spark, Hadoop, Dask)
  • Software engineering principles (testing, scalability, performance optimization)
  • Cross-functional communication and customer engagement
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STR

STR develops advanced autonomous systems, sensors, and software solutions for national security and defense applications, including multi-agent autonomy, radar and imaging systems, and cyber-physical system analysis tools. The company is hiring for leadership roles in autonomy and controls, software engineers, embedded systems developers, and co-op positions across its engineering teams.

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Likely interview questions

  • Describe a time you deployed a machine learning model to production. What challenges did you face in transitioning from prototype to product?
  • How would you approach handling sparse, noisy, or high-dimensional data in a real-world scenario?
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