Staff Machine Learning Engineer - Applied AI

About the role:

Applied AI is a horizontal AI team at Uber partnering with product and platform teams across the company to deliver cutting-edge machine learning solutions for core business problems. We specialize in areas like Generative AI, Computer Vision, Personalization, and the ML infrastructure needed to scale these systems in production.

We’re looking for a Staff Engineer with deep expertise in machine learning, generative AI, and ML systems design to lead technically complex projects and influence the architecture of ML at Uber. This role also values expertise in speech and audio ML, which will play a key role in building the next generation of voice-based generative AI solutions.

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:

• Lead technical execution of projects spanning classical ML, deep learning, and generative AI (e.g., LLMs, multimodal models).
• Define and influence technical direction for Applied AI initiatives, including system design, model architecture, and infrastructure.
• Collaborate with product, science, and engineering teams to align ML innovations with business impact.
• Champion best practices in ML development: experimentation workflows, evaluation, deployment, monitoring, and responsible AI.
• Mentor engineers across Applied AI and partner orgs, raising the technical bar through leadership and guidance.

Basic Qualifications:

• 10+ years of industry experience in ML 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 (e.g., transformers, LLMs, diffusion).
• Experience designing and scaling ML systems or platforms, including training pipelines, serving infrastructure, and model lifecycle tooling.
• Fluency in ML frameworks (e.g., PyTorch, TensorFlow, JAX) and development in Python and/or scalable backend languages (e.g., 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 and generative models into product experiences (e.g., conversational assistants, summarization, multimodal AI).
• Demonstrated experience with speech and audio ML: ASR (automatic speech recognition), TTS (text-to-speech, expressive voice synthesis), Voice embeddings (speaker verification, personalization), Noise robustness & enhancement for real-world audio
• Experience optimizing models for real-time or resource-constrained environments (mobile, edge, or embedded systems).
• Track record of technical leadership in multi-disciplinary ML projects involving engineering, data science, and product.

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.