Uber Sr Staff ML Engineer - Applied AI 

United States, West Virginia 

720460056

This is a highly visible and impactful role where you'll combine technical leadership, hands-on system design, and a product-first mindset to accelerate AI innovation across Uber.

• Drive the technical strategy and architecture for applied AI solutions spanning supervised learning, deep learning, and generative AI.
• Design and deliver scalable, production-ready ML systems, balancing experimentation velocity with engineering rigor.
• Partner with engineering, product, and data science leaders to translate ambiguous problems into concrete technical plans.
• Act as a thought leader and technical mentor, elevating engineering standards and fostering a strong culture of technical excellence and collaboration.
• Work across boundaries with platform and infra teams to influence the evolution of Uber’s ML tooling and infrastructure.
• Proactively evaluate new technologies, open-source solutions, and research developments — and guide their responsible integration into Uber’s stack.
• Play a key role in setting technical vision for how generative AI (e.g., LLMs, multimodal models) can unlock new capabilities for Uber’s products.

• 12+ years of experience in software engineering or machine learning, with a proven track record of delivering ML solutions at scale.
• Deep expertise in machine learning algorithms, deep learning architectures, and generative AI (e.g., LLMs, transformers, diffusion models).
• Strong system design skills with experience building and optimizing ML infrastructure for training, serving, and monitoring models in production.
• Demonstrated experience in technical leadership roles — driving cross-team initiatives, mentoring engineers, and setting technical direction.
• Fluency with modern ML development tools (e.g., PyTorch, TensorFlow, JAX, MLFlow, Ray) and cloud-native infrastructure.
• Strong product sense with the ability to prioritize for business impact.
• Excellent written and verbal communication skills, including the ability to influence across technical and non-technical audiences.

• PhD in Computer Science, Machine Learning, Statistics, or related field.
• Experience integrating generative AI into real-world products, such as co-pilots, summarization, or multimodal retrieval/generation systems.
• Hands-on experience building or contributing to ML platforms, experimentation frameworks, or model lifecycle management tools.
• Experience working in a horizontal platform or AI org serving multiple product teams.

* Accommodations may be available based on religious and/or medical conditions, or as required by applicable law. To request an accommodation, please reach out to .