How can Uber plan pricing and incentives to meet financial goals while keeping its marketplace healthy and growing? That’s the challenge our Mobility Simulation and Planning (MSP) organization tackles within Marketplace.
MSP builds forecasting, simulation, and optimization products that help Operations teams translate financial targets into pricing and incentive policies across hundreds of cities. Our planning product, Planner, brings these capabilities together to generate actionable price plans. Supporting this work requires scalable model training and simulation, efficient distributed computing, and reliable workflows for generating and deploying plans.
The MSP Infra team builds the data and computing foundations that make these capabilities possible. We partner with engineers, scientists, and shared-platform teams to turn complex modeling workflows into reliable, scalable, reusable infrastructure.
We’re looking for a Senior Software Engineer focused on ML infrastructure. You’ll build the frameworks, distributed execution capabilities, and developer tooling that help scientists develop, train, evaluate, and productionize models. Your work will make it easier to move from an experimental model to a dependable production workflow and to run the simulations and backtests needed to evaluate marketplace decisions.
How do we scale training and simulation across CPUs and GPUs? How do we make experiments reproducible and performance bottlenecks easy to diagnose? How do we support new modeling approaches without creating a separate infrastructure stack for every use case? You’ll solve these problems through hands-on engineering, close collaboration with modeling teams, and end-to-end ownership of production systems.
What you’ll do- Build shared ML frameworks: Design and evolve reusable interfaces and libraries for model development, training, inference, evaluation, and backtesting.
- Scale distributed workloads: Build and optimize execution capabilities for training, simulation, and optimization across CPU and GPU infrastructure.
- Accelerate experimentation: Develop tooling for hyperparameter tuning, experiment tracking, reproducibility, and performance analysis so scientists can evaluate ideas faster.
- Simplify productionization: Create reliable orchestration, configuration, dependency management, and deployment patterns that reduce the gap between experimentation and production.
- Improve performance and efficiency: Profile workloads, diagnose compute and memory bottlenecks, and make thoughtful tradeoffs across runtime, resource utilization, reliability, and cost.
- Build on shared platforms: Partner with Uber’s ML and compute platform teams to adopt and extend common capabilities rather than maintain isolated infrastructure.
- Own systems and raise the engineering bar: Lead projects from design through rollout and operations, participate in on-call and incident response, and mentor engineers through design and code reviews.
- 6+ years of professional software engineering experience, including work on ML infrastructure, distributed computing, or production platforms for computationally intensive workloads.
- Strong programming skills in Python 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 performance fundamentals, including 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.
- Familiarity with ML frameworks such as PyTorch, PyTorch Lightning, or LightGBM.
- Experience with distributed training, GPU performance profiling, or CPU/GPU workload optimization.
- Experience building hyperparameter tuning, experiment tracking, or model evaluation infrastructure.
- Experience supporting large-scale simulation, backtesting, forecasting, or optimization workloads.
- Familiarity with workflow orchestration, containerized execution, CI/CD, and build or dependency-management systems.
- Experience designing shared frameworks or developer platforms adopted by multiple modeling teams.
- A track record of measurable improvements in training or inference performance, infrastructure reliability, compute efficiency, or model-development productivity.
Responsibilities
For New York City, NY-based roles: The base salary range for this role is USD $202,000 per year - USD $224,000 per year.
For San Francisco, CA-based roles: The base salary range for this role is USD $202,000 per year - USD $224,000 per year.
For Seattle, WA-based roles: The base salary range for this role is USD $202,000 per year - USD $224,000 per year.
For Sunnyvale, CA-based roles: The base salary range for this role is USD $202,000 per year - USD $224,000 per year.
For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits.
About UsReady to Ride?
This isn't the kind of place where you follow a playbook — it's where you help write one. If you're driven by impact, energized by challenge, and ready to shape how the world moves — we'd love to hear from you.
You may be eligible for bonuses, equity, and other compensation, as well as a range of benefits. Explore our benefits.
Offices remain key to collaboration and Uber's culture. Unless approved for full remote work, employees must spend at least 50% of their time in-office. Some roles, like those at greenlight hubs, require full-time in-office presence. Ask your Recruiter for details about this role's requirements.
Uber is proud to be an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form.
Uber San Francisco, California, USA Office
Uber's a hybrid work environment and employees target spending 50% of their time in the office.
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