Staff Software Engineer (L6) / TLM - Developer Productivity - Platform Systems, AIMS Engineering

Overview

Own developer productivity for AIMS: the build, test, and iteration loop used daily by ML researchers and engineers. For TLM candidates, this includes leading and growing a small team of developer productivity engineers.

What you'll do

• 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.
• For TLM candidates: lead and grow a team of developer productivity engineers by setting direction, hiring, coaching, and supporting career growth, while staying hands-on in design and code.
• Identify friction in day-to-day engineering and research workflows firsthand, and design tooling and abstractions that remove it at the root.
• 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 high-leverage productivity investments, and ship tools people adopt.
• Build and maintain internal developer platforms and self-service tooling that reduce the operational burden on individual teams.
• Instrument developer workflows to measure productivity (build times, iteration speed, time-to-first-experiment) and use that data to prioritize investment.
• Drive adoption of new tooling through documentation, migration support, and hands-on partnership with teams.
• 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.

What you'll need

• Significant experience building and operating developer productivity infrastructure (build systems, CI/CD, developer environments, or internal platforms) at scale.
• Experience leading or mentoring engineers, as a manager or tech lead, and comfort staying hands-on technically while growing others.
• 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, building trust with ML researchers directly.
• Comfortable moving between low-level systems work (build graphs, compilers, runtime performance) and higher-level platform and API design.

Nice to have

• 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 (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.

Details

• Location: USA, Remote.
• Compensation range: $600,000.00 - $1,066,000.00 annual salary, varying by location.