Remain hands-on with performance testing and analysis, developing a deep understanding of existing workflows and using that knowledge to guide platform investments and technical decisions.
Establish engineering approaches that improve the reliability, scalability, observability, maintainability, and reproducibility of large benchmark campaigns.
Lead the diagnosis of complex, cross-layer issues spanning applications, Linux systems, containers, distributed jobs, compute resources, networking, and storage.
Build strong partnerships with performance engineers, QA teams, product teams, and other customers; create alignment across organizations and drive high-impact ideas and projects from concept through adoption.
Provide technical leadership through architecture and code reviews, clear decision-making, high engineering standards, and mentoring of other engineers.
Identify emerging technologies, including AI-assisted automation, and determine where they can deliver meaningful improvements in benchmark creation, failure triage, data analysis, or engineering productivity.
Contribute to the strategy and development of internal and open-source infrastructure projects, and help grow their adoption across teams.
What We Need to See
Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or equivalent practical experience.
6+ years of relevant software engineering experience in system software, infrastructure, developer platforms, distributed systems, or production automation.
Strong Python programming and software engineering expertise, with a track record of building and operating production-quality tools, services, or automation frameworks.
Solid understanding of Linux and system-level concepts such as processes, concurrency, networking, storage, resource management, and failure handling.
Extensive experience with containers and workload orchestration, scheduling, or distributed computing platforms, including the design of reliable systems with clear interfaces, testing, observability, and recovery behavior.
Knowledge of machine learning, AI, or accelerated-computing workloads and experience reasoning about their performance, quality, and operational tradeoffs.
Proven technical leadership on sophisticated, multi-functional projects, including defining architecture, managing technical risk, resolving ambiguity, and driving solutions through delivery and adoption.
Strong analytical and problem-solving abilities, with the judgment to prioritize effectively, manage multiple initiatives, and adapt in a dynamic, constantly evolving environment.
Excellent communication, organizational, and influencing skills, with the ability to align globally distributed collaborators and drive decisions.
A record of mentoring engineers, elevating engineering quality, and helping teams make better technical decisions.
Ways to Stand Out From the Crowd
Experience leading major initiatives in GPU or AI infrastructure, distributed training or inference, model evaluation, or performance benchmarking.
Experience architecting workflow engines, schedulers, experiment platforms, test frameworks, or developer infrastructure used by multiple teams.
Experience operating distributed or cloud-native systems at scale, including performance profiling, capacity analysis, resource scheduling, or multi-node workloads.
Practical experience establishing AI-agent, tool-calling, or coding-agent strategies that improved engineering workflows at team or organizational scale.
Demonstrated success turning loosely defined, cross-organizational problems into durable platforms or programs with measurable engineering impact.
We have some of the most forward-thinking and hardworking people in the world working for us. If you are creative, autonomous, and passionate about providing technical leadership while remaining hands-on in building systems that make complex AI performance work repeatable and scalable, we want to hear from you.
