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Staff ML Engineer

AT Uber
Uber

Staff ML Engineer

San Francisco, CA

About the Role

It is a challenging yet rewarding job. You will have a lot of opportunities to work with product managers, data scientists and of course engineers from other teams. You will participate in the whole development cycle of a software product from product scoping, architecture design, software implementation, to productionisation, and learn how to iterate a product for greater success.

We own a few products that directly impact Uber's top and bottom lines. We are a data-driven team, and you will be able to see the impact of your work reflected in Uber's earning report, such as gross booking and profits.

The least thing you need to worry about is scope and visibility. We have many different roles and levels in the team, and need a broad range of skills such as machine learning, big data, optimization, and infrastructure. This is a unique opportunity to grow your career and do highly impactful fun things at the same time.

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What the Candidate Will Do:

  • Build backend ML systems & data processing pipelines that will simulate and optimize Uber's pricing strategies.
  • Collaborate with data science and engineering teams to identify issues, brainstorm solutions, scoping, designing, implementing and testing improvements.
  • Analyze ML model performance and propose ways to improve. Dive deep into the data queries and pipelines to root cause data quality issues.
  • Write high-quality, modular and maintainable code and constantly improving code quality through refactoring and/or advocating for best practices.
  • Review code and designs and provide feedback.

Basic Qualifications:

  • Experience in using Python (pandas, scipy, or numpy, etc) for scientific computations and object-oriented programming.
  • Experience in using SQL (Presto, Hive, or Spark, etc) for data querying and ETL.
  • Experience in applying machine learning models to solve real-world problems.

Preferred Qualifications:

  • PhD or MS in Computer Science, Engineering, Mathematics or related field
  • Minimum 5 years of Machine Learning Engineering work experience.
  • Experience in optimization problem solving, deep learning, and/or causal inference.
  • Experience working with Generative AI

For San Francisco, CA-based roles: The base salary range for this role is USD$218,000 per year - USD$242,000 per year.

For Sunnyvale, CA-based roles: The base salary range for this role is USD$218,000 per year - USD$242,000 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. You will also be eligible for various benefits. More details can be found at the following link https://www.uber.com/careers/benefits.

Uber is proud to be an Equal Opportunity/Affirmative Action 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.

Offices continue to be central to collaboration and Uber's cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.

Client-provided location(s): San Francisco, CA, USA; Sunnyvale, CA, USA
Job ID: Uber-131547
Employment Type: Full Time

Perks and Benefits

  • Health and Wellness

    • Health Insurance
    • Health Reimbursement Account
    • Dental Insurance
    • Vision Insurance
    • Life Insurance
    • FSA With Employer Contribution
    • Fitness Subsidies
    • On-Site Gym
    • Mental Health Benefits
  • Parental Benefits

    • Fertility Benefits
  • Work Flexibility

    • Flexible Work Hours
    • Remote Work Opportunities
    • Hybrid Work Opportunities
  • Office Life and Perks

    • Casual Dress
    • Pet-friendly Office
    • Snacks
    • Some Meals Provided
    • On-Site Cafeteria
  • Vacation and Time Off

    • Paid Vacation
    • Unlimited Paid Time Off
    • Paid Holidays
    • Personal/Sick Days
    • Sabbatical
    • Volunteer Time Off
  • Financial and Retirement

    • 401(K)
    • Company Equity
    • Performance Bonus
  • Professional Development

    • Work Visa Sponsorship
    • Associate or Rotational Training Program
    • Promote From Within
    • Mentor Program
    • Access to Online Courses
  • Diversity and Inclusion

    • Employee Resource Groups (ERG)
    • Diversity, Equity, and Inclusion Program