Senior ML Infra Engineer — Scale, Reliability & Impact

Uber

Seattle (WA)

On-site

USD 202,000 - 224,000

Full time

9 days ago

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Benefits offered by this job

Bonus program

Equity award

401(k) plan

Benefits

Job summary

Uber is seeking a Senior Software Engineer for ML Data & Backend in Seattle. You will architect mission-critical ML infrastructure, from low-latency data pipelines to scalable backend systems powering trust across our marketplace.

You’ll design and implement long-lasting artifacts, mentor engineers, and partner with Product, Science, and Ops to translate requirements into production roadmaps. The role emphasizes leadership, reliability, and performance in a fast-paced environment.

Qualifications

6+ years of experience building developing ML models to solve business problem.
Bachelor’s degree in Computer Science, Computer Engineering, or related fields.
Familiar with modern AI/ML frameworks (e.g., PyTorch, Tensorflow).
Hands-on experience developing foundational models or Large Language Models (LLMs) for user facing applications, marketplace systems, recommendation engines, ad-tech platforms, or e-commerce/shopping infrastructures at scale.
Solid understanding of core machine learning algorithms, their applications, and the underlying systems/infrastructures required to train and serve them.
Proven track record in MLOps and ML Infrastructure (e.g., container orchestration via Kubernetes, data pipeline design, distributed workflow systems, and large-scale job scheduling).

Responsibilities

System Design & Architecture: Design and build long-lasting engineering artifacts that reduce complexity and increase developer velocity across the organization.
Complex Problem Solving: Solve messy, high-impact problems at the intersection of low latency and high correctness, often without a clear starting point or obvious solution.
Technical Leadership: Lead organization-wide development and adoption of key frameworks while role-modeling coding best practices and high-quality design reviews.
Cross-Functional Collaboration: Partner closely with Product, Science, and Ops to translate ambiguous business requirements into production-ready technical roadmaps.
Mentorship & Culture: Mentor and guide other engineers, acting as a technical brand ambassador and raising the bar for engineering excellence across the organization.