Staff ML Engineer - Applied AI @ Uber

we're looking for a Staff Engineer with deep experience in classical machine learning, generative AI, and ML systems design to help build and scale high-impact AI solutions. In this role, you'll lead technically complex projects, influence the architecture of ML systems, and collaborate cross-functionally to drive innovation and impact across multiple Uber surfaces.

This is a great opportunity for a strong technical leader who thrives in a fast-paced, product-driven environment and wants to be at the forefront of applied AI at scale.

What you'll Do

• Design and implement ML-driven systems that power core Uber experiences, with a focus on scalability, reliability, and performance.
• Lead the technical execution of key projects involving classical ML, deep learning, and generative AI technologies (eg, LLMs, multimodal models).
• Collaborate closely with product, data science, and infrastructure teams to develop AI solutions from ideation through production deployment.
• Contribute to and influence the technical direction for Applied AI, particularly around system design, model architecture, and infrastructure decisions.
• Champion engineering best practices in ML development including experimentation workflows, model versioning, evaluation, monitoring, and responsible AI.
• Provide mentorship to engineers on the team and across partner orgs to help raise the technical bar.

Basic Qualifications

• 10+ years of industry experience in machine learning or software engineering, with a proven record of delivering ML solutions to production.
• Strong knowledge of machine learning, deep learning, and exposure to generative AI techniques (eg, transformers, LLMs, diffusion).
• Experience designing and scaling ML systems or platforms, including training pipelines, serving infrastructure, and model lifecycle tooling.
• Fluency in ML frameworks (eg, PyTorch, TensorFlow, JAX) and development in Python and/or scalable backend languages (eg, Java, Go).
• Excellent collaboration and communication skills with the ability to work across teams and functions.

Preferred Qualifications

• PhD in Computer Science, Machine Learning, or a related field.
• Hands-on experience integrating LLMs or generative models into product experiences (eg, summarization, personalization, automation).
• Familiarity with MLOps, experimentation frameworks, or ML observability tools.
• Track record of technical leadership in multi-disciplinary projects involving engineering, data science, and product.