Uber is looking to solve core business problems by delivering cutting-edge machine learning solutions, specifically focusing on Generative AI, Computer Vision, and Personalization, and the ML infrastructure required to scale these systems in production. The role aims to lead technically complex projects and influence the architecture of ML at Uber, with a particular emphasis on speech and audio ML for next-generation voice-based generative AI solutions.
Requirements
- 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).
- 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).
Responsibilities
- 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.
Other
- Excellent collaboration and communication skills with the ability to work across teams and functions.
- Track record of technical leadership in multi-disciplinary ML projects involving engineering, data science, and product.
