Senior Machine Learning Engineer, Applied AI & Data

The Company

Git Kraken is the developer experience (Dev Ex) platform of choice for more than 40 million developers and 100,000 organizations globally. Combining built-in AI and powerful workflow orchestration, Git Kraken empowers development teams to eliminate unnecessary toil, streamline collaboration, and accelerate productivity. Git Kraken’s seamless integrations with leading Git providers, issue tracking tools, and AI solutions make it the most versatile Dev Ex platform available across desktop, command line, IDE, web, and mobile environments.

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At Git Kraken, our goal is to help developers and their teams focus, create, and collaborate while minimizing distractions, context switching, and wasted time. Our developer experience platform supports millions of developers across desktop, command line, IDE, browser, web, and mobile.

We’re looking for a pragmatic, startup‑minded Senior Machine Learning Engineer or Applied Data Scientist who can take an idea from concept to production. Sometimes that idea will come from the data. Sometimes it will come from the business. In both cases, you’ll be expected to determine what’s possible, identify the fastest credible path forward, and ship solutions that create measurable impact.

This is a high‑ownership role for someone comfortable working across data, product, and engineering. You should be able to frame ambiguous problems, explore messy data, build models or heuristics, integrate with production systems, measure outcomes, and iterate quickly. We care about practical impact, traction, and speed of learning. We are not looking for someone who waits for perfect specs or over‑polishes a solution before proving it matters.

• Identify high‑value opportunities from product, customer, and operational data
• Evaluate ambiguous ideas quickly and determine what is feasible, useful, and worth shipping
• Build practical 80/20 solutions that create leverage quickly, then refine them based on traction
• Own end‑to‑end execution across data exploration, modeling, experimentation, backend integration, and productization
• Partner with engineering, product, design, and leadership to turn rough ideas into shipped capabilities
• Use ML, analytics, heuristics, and automation pragmatically rather than forcing a model where one is not needed
• Define success metrics, instrument outcomes, and improve solutions based on real‑world usage
• Help shape how Git Kraken uses AI and data to improve developer workflows, team velocity, and product experience

• Languages:
  
  Python (for data/ML execution), alongside Go and Type Script across our core product and backend environments.
• Data & Infrastructure:
  Snowflake for data warehousing, AWS for cloud infrastructure, and Datadog for monitoring and observability.
• AI Ecosystem & Dev Ex:
  We live and breathe developer experience. We heavily leverage and build around modern AI development tools and LLMs like Cursor, Claude Code, and Codex to accelerate execution and shape the future of workflows.

• Deep experience in machine learning, applied AI, or a similarly hands‑on product data role at a Senior level
• A track record of shipping data or ML‑powered capabilities into real products or operational workflows
• Comfort moving from messy problem statements to practical execution without a lot of structure
• Ability to work across the stack, not just in notebooks
• Strong product judgment and a bias toward simple solutions that deliver measurable value
• Experience deciding whether a problem is best solved with ML, rules, analytics, automation, or workflow design
• Ability to balance speed and rigor, including knowing when “good enough to learn” is the right answer
• Strong communication skills and the ability to explain tradeoffs clearly to technical and non‑technical partners
• Ownership mindset: you don’t wait for perfect specs, and you follow through from idea to impact

• You’ve built and shipped data or ML‑powered features, not just analyses
• You can prototype quickly and are comfortable refining after launch
• You know how to avoid getting buried in edge cases before…