Base Salary
$202k - $224k/yr
Responsibilities
- Design and evolve reusable ML frameworks and libraries for model development, training, inference, evaluation, and backtesting.
- Build and optimize distributed execution capabilities for training, simulation, and optimization across CPU and GPU infrastructure.
- Develop tooling for hyperparameter tuning, experiment tracking, reproducibility, and performance analysis.
- Create reliable orchestration, configuration, dependency-management, and deployment patterns for production ML workflows.
- Profile workloads and improve runtime, resource utilization, reliability, and cost.
- Partner with ML, compute-platform, engineering, and data-science teams to extend shared infrastructure.
- Lead projects from design through rollout and operations, participate in on-call and incident response, and mentor engineers through design and code reviews.
Requirements
- 6+ years of professional software engineering experience, including ML infrastructure, distributed computing, or production platforms for computationally intensive workloads.
- Strong Python programming skills and experience building maintainable, well-tested software.
- Experience designing, building, and operating distributed systems or workflow execution infrastructure.
- Experience supporting production ML workflows such as model training, batch inference, evaluation, or deployment.
- Understanding of parallelism, memory management, data movement, and resource utilization.
- Experience delivering technical projects from design through production rollout 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.
- Experience with Ray or comparable distributed computing frameworks is preferred.
- Familiarity with PyTorch, PyTorch Lightning, or LightGBM is preferred.
- Experience with distributed training, GPU performance profiling, CPU/GPU workload optimization, hyperparameter tuning, experiment tracking, model evaluation infrastructure, or large-scale simulation and optimization workloads is preferred.
- Familiarity with workflow orchestration, containerized execution, CI/CD, and build or dependency-management systems is preferred.
- Experience designing shared frameworks or developer platforms adopted by multiple modeling teams is preferred.
- Demonstrated improvements in training or inference performance, infrastructure reliability, compute efficiency, or model-development productivity are preferred.
Benefits
- Eligible for Uber’s bonus program and potential equity awards and other compensation.
- Eligible for a 401(k) plan and various employee benefits.
Categories
About Uber
We are Uber. The go-getters. The kind of people who are relentless about our mission to help people go anywhere and get anything and earn their way. Movement is what we power. It’s our lifeblood. It runs through our veins. It’s what gets us out of bed each morning. It pushes us to constantly reimagine how we can move better. For you. For all the places you want to go. For all the things you want to get. For all the ways you want to earn. Across the entire world. In real time. At the incredible speed of now. The idea for Uber was born on a snowy night in Paris in 2008, and ever since then our DNA of reimagination and reinvention carries on. We’ve grown into a global platform powering flexible earnings and the movement of people and things in ever expanding ways. We’ve gone from connecting rides on 4 wheels to 2 wheels to 18-wheel freight deliveries. From takeout meals to daily essentials to prescription drugs to just about anything you need at any time and earning your way. From drivers with background checks to real-time verification, safety is a top priority every single day. At Uber, the pursuit of reimagination is never finished, never stops, and is always just beginning.