Sr Software Engineer - ML Infrastructure
uberSeattle (WA)
Sr Software Engineer - ML Infrastructure
Posted today
uberSeattle (WA)
SENIORITY
Lead
About the role
Overview:
In this role you will help Uber scale ML infrastructure for MSP, supporting forecasting, simulation, and optimization that translate financial targets into pricing policies. You work closely with modeling teams to move models from experimentation into production and run backtests to evaluate marketplace decisions. You’ll build and evolve shared frameworks, enable scalable training and simulation across CPU/GPU, and improve reliability and efficiency of end-to-end workflows. This is a hands-on, end-to-end impact role focused on making production-ready ML workflows fast, reproducible, and scalable. Join a team that values collaboration and practical solutions to complex optimization problems. Compensation / Benefits bonus program equity 401(k) plan various benefits
Responsibilities:
Design and evolve reusable ML development, training, inference, evaluation, and backtesting interfaces Scale distributed workloads for training, simulation, and optimization on CPU and GPU resources Develop tooling for hyperparameter tuning, experiment tracking, reproducibility, and performance analysis Create reliable deployment patterns and orchestration to shorten the path from experiment to production Profile workloads, diagnose bottlenecks, and optimize for performance, reliability, and cost Collaborate with ML platform teams to adopt shared capabilities and avoid isolated stacks Lead projects end-to-end from design to rollout, participate in on-call, and mentor engineers
Key requirements:
6+ years of software engineering experience with ML infrastructure, distributed computing, or production platforms Strong Python programming and maintainable, well-tested software development Experience designing, building, and operating distributed systems or workflow execution infrastructure Experience supporting production ML workflows (training, batch inference, evaluation, deployment) Understanding of performance fundamentals (parallelism, memory management, data movement, resource utilization) Track record delivering technical projects from design to production and ongoing operations Ability to communicate technical tradeoffs and collaborate across engineering, data science, and platform teams Bachelor’s or Master’s degree in Computer Science, Engineering, or equivalent practical experience strong communication cross-functional collaboration mentoring and leadership Python distributed systems / workflow execution infrastructure ML infrastructure
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