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Senior Software Engineer (AI Evals)

Sentry · United States

External listingfull-time18 days ago

About The Role

Join Sentry's AI/ML team as a Senior Software Engineer, where you'll build the evaluation infrastructure that measures the accuracy, reliability, and real-world performance of our AI systems. This critical role involves designing datasets, benchmarks, and test harnesses to ensure our AI-powered features behave correctly and predictably as they scale. You'll collaborate closely with applied AI engineers and product leaders, own the evaluation lifecycle for major AI initiatives, and enjoy a range of benefits including competitive compensation, medical insurance, flexible PTO, and more.

  • Design and build robust evaluation frameworks to measure accuracy, reliability, regressions, and edge cases in AI systems.
  • Create and curate high-quality datasets, golden test cases, and benchmarks grounded in real production data.
  • Partner closely with applied AI engineers and product leaders to define what “good” looks like and translate it into measurable criteria.
  • Care deeply about correctness, rigor, and measurement in AI systems
  • Like building foundational infrastructure that unlocks faster iteration and higher confidence for the entire AI team
  • Enjoy turning fuzzy product goals and model behavior into concrete tests and metrics
  • Thrive in cross-functional environments and enjoy influencing model design through better evaluation
  • Familiarity with modern ML systems and evaluation techniques (e.g., offline metrics, online evaluation, regression testing for models or prompts)
  • Minimum 5+ years of professional experience with a Bachelor’s degree in computer science, machine learning, or a related field
  • Bonus: experience evaluating LLMs, agentic systems, or AI-assisted developer tools
  • Comfort writing production-quality code (we use Python and TypeScript)
  • Experience building testing, evaluation, or data infrastructure for complex systems (AI/ML experience strongly preferred)
  • Experience working with structured and unstructured datasets, labeling workflows, or data quality pipelines

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