Senior Machine Learning Engineer

  • San Francisco, CA
  • Posted 23 hours ago | Updated 12 hours ago

Overview

On Site
USD 198,000.00 - 220,000.00 per year
Full Time

Skills

Productivity
Software Engineering
Generative Artificial Intelligence (AI)
Visualization
Data Storage
C++
Java
Python
Artificial Intelligence
Optimization
Research
Data Analysis
Backend Development
Machine Learning (ML)
Debugging
Data Processing
Apache Spark
Apache Hive
Information Retrieval
Law
Legal
Collaboration

Job Details

About the Role

We are looking for a highly-motivated software engineer to join our Data GenAI team, which focuses on providing machine learning/GenAI solutions to improve both Uber's data utilization productivity and large scale data processing efficiency. You will work on a variety of projects including GenAI applications, predictive machine learning models and infrastructure software engineering. It is a special opportunity for you to work in the cross-road of machine learning and data infrastructure and make a great impact on the company.

What the Candidate Will Do ----

1. Build GenAI multiagent system to provide revolutionary tooling for thousands of internal users.
2. Build predictive machine learning models for a range of applications to optimize uber's petabyte scale data processing system.
3. Collaborate with a world-class data infrastructure team, provide insights via data analysis, visualization and other investigations.
4. Explore novel ideas (e.g. LLM finetuning) towards improving Uber's data storage and computation efficiency.

\\-\\-\\-\\- Basic Qualifications ----

1. 6+ years of full-time engineering experience
2. Proficiency in at least one programming language (e.g., C++, Java, Python, or Go).
3. 2+ years of experience in one or more of the following areas: machine learning, artificial intelligence, optimization, operational research, or related technical fields
4. Familiar with data analysis techniques and backend development.

\\-\\-\\-\\- Preferred Qualifications ----

1. Experience productionizing applied machine learning solutions towards solving business or product challenges
2. Knowledge with internal tooling development or data/ML infrastructure
3. Experience developing and debugging in large scale data processing frameworks such as Apache Spark, Hive, and/or Presto
4. Experience with the recommendation system or information retrieval system is a big plus.
5. Experience with LLM is not required but would be a big plus

For San Francisco, CA-based roles: The base salary range for this role is USD$198,000 per year - USD$220,000 per year.

For Sunnyvale, CA-based roles: The base salary range for this role is USD$198,000 per year - USD$220,000 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 [](;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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