Senior Staff Machine Learning Engineer - Trusted Identity

Overview

On Site
USD 257,000.00 - 285,500.00 per year
Full Time

Skills

Artificial Intelligence
Fraud
Innovation
Data Science
Leadership
Roadmaps
Systems Design
A/B Testing
Deep Learning
Shipping
Computer Science
Mathematics
Conflict Resolution
Problem Solving
Statistics
Optimization
Machine Learning (ML)
TensorFlow
PyTorch
JAX
Programming Languages
Python
Apache Spark
SQL
Java

Job Details

About the Role

We are looking for an experienced Senior Staff Machine Learning Engineer to join the Account Integrity team within Trusted Identity engineering org at Uber.

The Trusted Identity org plays a crucial role in our mission empower the users with secure and seamless digital experiences by establishing industry-leading standards for identity verification, and fraud prevention, while proactively safeguarding against the evolving landscape of AI-driven fraud.Our continued obsession for innovation in the space is essential to ensure safety as people interact on Uber's platform.

What the Candidate Will Need / Bonus Points

\\-\\-\\-\\- What the Candidate Will Do ----

1. Work with product, data science, and eng leadership to shape the technical roadmap and problem formulations for the team.
2. Leverage algorithmic knowledge in machine. learning/optimization/statistics to design robust engineering solutions to positively impact Uber's business.
3. Shape the MLE role and uplevel MLE talents in the org.
4. 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 ----

1. 10+ years of industry experience developing machine learning models ( both classical and deep learning) and shipping ML solutions to production.
2. Master's degree in Computer Science, Engineering, Mathematics or related field
3. Strong problem-solving skills, with expertise in ML methodologies
4. Experience in applying ML, statistics, or optimization techniques to solve large-scale real-world problems
5. 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 ----

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

For New York, NY-based roles: The base salary range for this role is USD$257,000 per year - USD$285,500 per year.

For San Francisco, CA-based roles: The base salary range for this role is USD$257,000 per year - USD$285,500 per year.

For Seattle, WA-based roles: The base salary range for this role is USD$257,000 per year - USD$285,500 per year.

For Sunnyvale, CA-based roles: The base salary range for this role is USD$257,000 per year - USD$285,500 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 [](;br>
Uber's mission is to reimagine the way the world moves for the better. Here, bold ideas create real-world impact, challenges drive growth, and speed fuels progress. What moves us, moves the world - let's move it forward, together.

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](;br>
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.
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