Senior Staff Machine Learning Engineer - Core Services Engineering

Uber; Seattle (WA); On-site; USD 267,000 - 297,000; Full time

Benefits offered by this job

Bonus program

Equity award

401(k) plan

Job summary

Uber is seeking a Senior Staff Machine Learning Engineer to join the Risk and Trust engineering organization. You will shape technical roadmaps, design robust ML solutions for fraud prevention and account integrity, and lead end-to-end ML product delivery at scale.

In this role, you will work on productionizing ML models, AB testing, and rollout strategies, while strengthening the team and cultivating talent. A strong background in ML methodologies and scalable data pipelines is essential.

Qualifications

• 10+ years of industry experience developing ML models and shipping to production.
• Master's degree in Computer Science, Engineering, Mathematics or related field.
• Strong problem-solving skills with expertise in ML methodologies.
• Experience applying ML, statistics, or optimization techniques to solve large-scale real-world problems.
• Experience with ML frameworks (TensorFlow, PyTorch, or JAX) and data pipelines; languages: Python, Spark SQL, Presto, Go, Java.

Responsibilities

• Shape the technical roadmap and problem formulations for the team with product and leadership.
• Apply ML, optimization, and statistics to design robust solutions impacting Uber's business.
• Elevate the MLE role and mentor talent within the organization.
• Own end-to-end ML product development: pipeline, system design, AB testing, rollout, and productionization.

Skills

ML expertise

Problem solving

Production ML

Algorithmic knowledge

Education

Master's degree

Tools

TensorFlow

PyTorch

JAX

Python

Spark SQL

Presto

Go

Java

Job description

We are looking for an experienced Senior Staff Machine Learning Engineer to join the Risk and Trust engineering organization at Uber. Our team plays a crucial role in empowering users with secure and seamless digital experiences by establishing industry-leading standards for identity verification, account integrity, and advanced fraud prevention. We proactively safeguard the platform against the evolving landscape of AI-driven fraud, ensuring safety and trust remain at the core of every interaction on Uber's platform. Our focus on fraud prevention is essential to protecting our users and maintaining the integrity of our global services.

About The Role

We are seeking a seasoned Senior Staff Machine Learning Engineer to join the Risk and Trust engineering organization at Uber, focusing on innovative fraud prevention and account integrity solutions. The Risk and Trust organization is central to Uber's mission of providing secure and seamless digital experiences. We focus on establishing industry-leading standards for identity verification, fraud prevention, and account integrity, while proactively defending against sophisticated, AI-driven threats. Our commitment to innovation in fraud prevention is essential to maintaining a safe and trusted environment for everyone who uses the Uber platform.

What You'll Do

• Work with product, data science, and eng leadership to shape the technical roadmap and problem formulations for the team.
• Leverage algorithmic knowledge in machine learning/optimization/statistics to design robust engineering solutions to positively impact Uber's business.
• Shape the MLE role and uplevel MLE talents in the org.
• Be responsible for the End to End of the product - ML model pipeline & system design, implementation, AB testing, and rollout. Work with the team to productionize the solutions at scale.

Basic Qualifications

• 10+ years of industry experience developing machine learning models ( both classical and deep learning) and shipping ML solutions to production.
• Master's degree in Computer Science, Engineering, Mathematics or related field
• Strong problem-solving skills, with expertise in ML methodologies
• Experience in applying ML, statistics, or optimization techniques to solve large-scale real-world problems
• Industry experience in ML frameworks (e.g. Tensorflow, Pytorch, or JAX) and complex data pipelines; programming languages such as Python, Spark SQL, Presto, Go, Java

Preferred Qualifications

• PhD degree in Computer Science, Engineering, Mathematics or related field
• Familiarity with multi-task learning, LLMs and anomaly detection
• Fraud domain knowledge

For San Francisco, CA-based roles: The base salary range for this role is USD $267,000 per year - USD $297,000 per year.

For Seattle, WA-based roles: The base salary range for this role is USD $267,000 per year - USD $297,000 per year.

For Sunnyvale, CA-based roles: The base salary range for this role is USD $267,000 per year - USD $297,000 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.

Ready 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.