Staff Software Engineer - ML Michelangelo
Role summary
Uber is seeking a Staff Software Engineer specializing in Machine Learning to design, build, and maintain scalable deep learning training systems and frameworks. This role involves empowering production teams to innovate and deploy state-of-the-art models, ensuring the reliability and efficiency of distributed training tools. The engineer will collaborate with cross-functional teams to deliver robust ML solutions, requiring expertise in Python, PyTorch, and distributed deep learning systems. The position is based in Sunnyvale, CA, with a competitive salary range and benefits.
About The Role
Partners with stakeholders and leads team efforts to build and maintain Machine Learning backend services and solutions to support user-facing products, downstream services, or infrastructure tools and platforms used across Uber.
What the Candidate Will Do ----
- Design and build tools to empower production teams to innovate and productionize state-of-the-art deep learning models at Uber.
- Develop and maintain scalable, end-to-end deep learning training systems and frameworks.
- Ensure distributed training tools are reliable, efficient, flexible to use for new production use cases.
- Collaborate with cross-functional teams including machine learning engineers, backend engineers, data scientists, and data engineers to deliver robust ML solutions for Uber.
Basic Qualifications
- Master in relevant fields (CS, EE, Math, Stats, etc.) AND 8+years full-time Software Engineering work experience in deep learning
- Proficiency in Python and PyTorch
- Expertise in designing, debugging, and optimizing distributed deep learning systems.
- Working experience of distributed training in PyTorch at Scale (e.g., data parallelism, model parallelism).
- Strong ability to translate complex DL requirements and problems into scalable solutions.
Preferred Qualifications
- Expertise in distributed training frameworks such as DDP, DeepSpeed, FSDP, or TorchRec.
- Familiarity with C++, Go or CUDA programming.
- Expertise in optimizing GPU/TPU training performance and data loading efficiency.
- Familiarity with large-scale distributed infrastructure tools like Ray, OpenAI Triton, PyTorch Lightning.
- Built and deployed end-to-end machine learning systems in production.
- Experience training large models (10B+ parameters), such as large recommendation systems or large language models (LLMs)
- PhD in relevant fields (CS, EE, Math, Stats, etc.)
For Sunnyvale, CA-based roles: The base salary range for this role is USD$232,000 per year - USD$258,000 per year. 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. More details can be found at the following link https://jobs.uber.com/en/benefits., For Sunnyvale, CA-based roles: The base salary range for this role is USD$232,000 per year - USD$258,000 per year. 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. More details can be found at the following link https://jobs.uber.com/en/benefits.
Sample Uber interview questions
- 1
Design a truck tracking system that supports filtering by truck number and includes an interface
system designmedium - 2
Design Uber Eats
system designmedium - 3
Given a sorted array of integers (which may include negatives), return the squares of the numbers
codingmedium - 4
Find the minimum characters to insert to make a string a palindrome
codingmedium - 5
Given an array of integers and a number N, find the length of the longest contiguous subarray such
codingmedium
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