Netflix is a company that aims to entertain the world through innovative storytelling and technology. They are seeking a Senior/Staff AI Software Engineer to enhance developer productivity for their AI systems, focusing on improving the build, test, and iteration processes for machine learning researchers and engineers. Responsibilities Own the end-to-end developer experience for AIMS ML practitioners: local and remote dev environments, build and test infrastructure, CI/CD pipelines, and the tools researchers use to move from idea to production experiment Identify friction in day-to-day engineering and research workflows firsthand, and design tooling and abstractions that remove it at the root rather than patching around it Design, build, and operate large-scale build and CI/CD systems that keep build, test, and iteration times fast as the codebase, model count, and headcount grow Partner directly with ML researchers and engineers embedded across AIMS teams to understand real workflows, prioritize the highest-leverage productivity investments, and ship tools people actually adopt Build and maintain internal developer platforms and self-service tooling that reduce the operational burden on individual teams, so they can focus on ML work instead of infrastructure upkeep Instrument developer workflows to measure productivity — build times, iteration speed, time-to-first-experiment — and use that data to prioritize where to invest next Drive adoption of new tooling through documentation, migration support, and hands-on partnership with teams; treat launch as the start of the work, not the end Set technical standards for developer tooling across AIMS and raise the engineering bar through design reviews and architectural guidance Evaluate, integrate, and productionize GenAI-powered developer tooling — intelligent build/test selection, automated code review, triage automation — where it measurably improves velocity, and build the guardrails that make it safe to rely on Qualification Developer productivity infrastructure Build systems CI/CD pipelines Developer environments Internal developer platforms Python JVM languages Scala Java Distributed build systems Bazel Buck Pants Distributed data/compute frameworks Spark Beam GenAI-powered developer tooling AI coding assistants Automated code review Agentic coding workflows Parallel computing Distributed computing Build graph optimization Compiler tooling Language tooling Static analysis High-performance computing environments Large-scale batch processing systems Technical program management Required Significant experience building and operating developer productivity infrastructure — build systems, CI/CD, developer environments, or internal platforms — at scale Strong software engineering fundamentals, with deep proficiency in Python and working proficiency in at least one JVM language (Scala, Java, or similar) Hands-on experience with distributed build systems (e.g., Bazel, Buck, Pants) and large-scale distributed data/compute frameworks (e.g., Spark, Beam) Working understanding of GenAI-powered developer tooling — AI coding assistants, automated code review, agentic coding workflows — and hands-on experience using these tools effectively in your own engineering practice, including judgment about where they help, where they don't, and how to validate their output Comfort with parallel and distributed computing, and experience operating systems at a scale where naive approaches stop working A track record of diagnosing developer friction from direct observation of how engineers actually work, not just from ticket queues, and shipping tooling that measurably improves it Ability to drive cross-team technical programs and earn adoption without formal authority — this role builds trust with ML researchers directly, not just with other infra engineers Comfortable moving between low-level systems work (build graphs, compilers, runtime performance) and higher-level platform and API design Preferred Experience with ML-specific developer tooling: experiment tracking, training pipeline orchestration, feature stores, or notebook-to-production workflows Experience designing or shipping GenAI-powered developer tooling as a product for other engineers — not just using it, but building it (e.g., internal coding assistants, automated review bots, agentic CI workflows) Contributions to open-source developer tooling, build systems, or distributed data processing projects Experience with compiler or language tooling, static analysis, or build graph/dependency optimization Experience operating high-performance computing environments or large-scale batch processing systems Benefits Health Plans Mental Health support A 401(k) Retirement Plan with employer match Stock Option Program Disability Programs Health Savings and Flexible Spending Accounts Family-forming benefits Life and Serious Injury Benefits Paid leave of absence programs Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full-time salaried employees are immediately entitled to flexible time off.