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Software Engineer - Simulation and Data

Job in San Francisco, San Francisco County, California, 94199, USA
Listing for: Uber
Full Time position
Listed on 2026-02-09
Job specializations:
  • IT/Tech
    Data Engineer, Systems Engineer, AI Engineer, Machine Learning/ ML Engineer
Job Description & How to Apply Below
Position: Staff Software Engineer - Simulation and Data

About the role:

We re seeking an exceptional Staff Engineer to lead the marketplace simulation, control systems and data infrastructure powering Uber s millions rides daily. This role demands a proven track record of extraordinary technical leadership and innovative solutions at world-class technology companies.

You’ll architect and scale supply pricing simulation infrastructure and control systems processing terabytes of data daily, build the next generation of joint simulation systems spanning rider pricing, driver pricing, and matching, and design clean interfaces between supply pricing and broader marketplace optimization systems. You’ll also build supply pricing data foundation and optimize batch and streaming pipelines. This work directly impacts marketplace efficiency and offers a unique opportunity to tackle deep technical challenges across data, real-time optimization, distributed systems, and marketplace dynamics.

What the Candidate will do:

You’ll be working on infrastructure that directly affects millions of drivers’ livelihoods and riders’ experiences every day. Our pricing systems process billions of batch and real-time decisions, balancing marketplace efficiency, driver earnings, and rider satisfaction. You’ll have the opportunity to:

  • Solve unique technical challenges at the intersection of batch and real-time optimization
  • Build marketplace simulation infrastructure that powers one of the world’s largest marketplace platforms
  • Work with cutting-edge pricing algorithms and ML systems that operate at unprecedented scale
  • Shape the technical direction of critical business infrastructure with direct P&L impact

Technical Leadership & Architecture

  • Architect simulation, optimization and control (PID controller) systems.
  • Drive innovation in data infrastructure design, build a data foundation, improve data quality, and improve the efficiency of compute-intensive jobs.
  • Design interfaces between driver pricing and other Marketplace optimization systems to ensure both accuracy and efficiency.

Cross-Functional Impact

  • Collaborate with engineering teams across Marketplace to build a top-quality joint simulation framework, improve simulation accuracy, and improve algorithm iteration velocity.
  • Partner with Data Science and Product teams to build a unified infrastructure supporting pricing algorithms and marketplace optimization

Engineering Excellence & Leadership

  • Mentor senior engineers and grow technical leadership capability across the marketplace organization
  • Establish engineering standards, team processes, architectural patterns, and operational excellence practices
Basic Qualifications:
  • Computer Science degree or equivalent technical education
  • 8+ years building large-scale backend or platform systems at Staff/Principal scope.
  • Experience operating compute-intensive and data-intensive systems at scale, including capacity planning and cost efficiency in cloud environments
  • Strong data infrastructure background, experience in optimizing data foundation and pipeline efficiency.
  • Strong system design and algorithmic problem-solving skills
  • Experience partnering with Data Science / ML teams
  • Proven technical leadership across teams and org boundaries

    • Drive architecture decisions across org boundaries
    • Mentor senior engineers
    • Set long-term technical direction and quality bar
Preferred Qualifications:
  • Background in pricing, ads, recommendations, or other areas involving optimization and control systems.
  • Hands-on experience designing or operating simulations and control systems

    • Improving simulation accuracy
    • Modernizing legacy simulation infrastructure with measurable improvements in accuracy, cost, or velocity
    • Strong understanding of simulation evaluation, including simulation bias, feedback loops, and calibration techniques
  • Familiarity with optimization techniques (e.g., stochastic optimization, Monte Carlo methods, heuristic search) applied at scale

  • Exceptional technical craftsmanship with a passion for solving complex, real-world problems at massive scale

  • Marketplace mindset:
    Understanding of supply/demand dynamics and real-time optimization challenges
  • Innovation and ownership:
    Ability to identify breakthrough opportunities and drive…
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