About the Role
Uber’s AI platform, Michelangelo, provides end-to-end infrastructure that enables ML engineers and data scientists to build, deploy, and scale machine learning solutions across all critical stages—feature engineering, distributed training, model inference, MLOps, resource management, and monitoring.
The ML Training team is a core pillar of Michelangelo, focused on building large-scale distributed training systems for multi-GPU/TPU environments. The team works across distributed systems, training frameworks, and ML infrastructure to enable efficient, reliable, and scalable model development.
As a Software Engineer II, you will design and build core components of Uber’s ML training platform. You will work on challenging distributed systems problems, contribute to system design, and collaborate closely with ML engineers and data scientists to deliver scalable AI solutions.
This role is ideal for engineers who are strong in systems and implementation, and want to work on impactful ML infrastructure at scale.
What the Candidate Will Need / Bonus Points
---- What the Candidate Will Do ----
- Design, build, and maintain components of distributed training systems for multi-GPU/TPU environments.
- Implement features and improvements for ML training infrastructure and platform services.
- Collaborate with ML engineers and data scientists to support model development and deployment workflows.
- Write clean, efficient, and maintainable code with proper testing and documentation.
- Debug and resolve issues in distributed systems and ML pipelines with guidance from senior engineers.
- Improve system reliability and performance through incremental enhancements.
- Participate in code reviews and design discussions to learn and apply engineering best practices.
- Contribute to team projects and support overall platform development efforts.
---- Basic Qualifications ----
- BS/MS in Computer Science or related field and 2+ years of Software Engineering experience (or PhD new graduates).
- Strong programming skills in Python, Java, Go, or C++.
- Experience building software systems or services (e.g., backend systems, data pipelines, or infrastructure).
- Familiarity with distributed systems fundamentals.
- Exposure to ML/DL frameworks (e.g., PyTorch, TensorFlow, JAX) or ML workflows.
---- Preferred Qualifications ----
- Experience with distributed systems or cloud-based infrastructure.
- Familiarity with ML infrastructure or training workflows.
- Exposure to distributed training technologies (e.g., DDP, FSDP, DeepSpeed).
- Basic understanding of GPU/TPU environments and accelerator hardware.
- Experience with data processing systems (e.g., Spark, Ray).
- Interest in performance optimization and system efficiency.
- Strong debugging and problem-solving skills.
For Seattle, WA-based roles: The base salary range for this role is USD$171,000 per year - USD$190,000 per year.
For Sunnyvale, CA-based roles: The base salary range for this role is USD$171,000 per year - USD$190,000 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. More details can be found at the following link https://jobs.uber.com/en/benefits.
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.
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.