Machine Learning Engineer II - Applied AI

About the Role

Applied AI is a horizontal AI team at Uber collaborating with business units across the company to deliver cutting-edge AI solutions for core business problems. We work closely with engineering, product and data science teams to understand key business problems and the potential for AI solutions, then deliver those AI solutions end-to-end. Key areas of expertise include Generative AI, Computer Vision, and Personalization.

We are looking for a strong ML engineer to be a part of a high-impact team at the intersection of classical machine learning, generative AI, and ML infrastructure. In this role, you'll be responsible for delivering Uber's next wave of intelligent experiences by building ML solutions that power core user and business-facing products.

---- What the Candidate Will Do ----

1. Solve business-critical problems using a mix of generative AI, classical ML, and deep learning.
2. Build generative AI applications (e.g., conversational assistants, text summarization, multimodal experiences) using large language models and related architectures.
3. Collaborate with product, science, and engineering teams to execute on the technical vision and roadmap for Applied AI initiatives.
4. Deliver high-quality, production-ready ML systems and infrastructure, from experimentation through deployment and monitoring.
5. Adopt best practices in ML development lifecycle (e.g., data versioning, model training, evaluation, monitoring, responsible AI).
6. Deliver enduring value in the form of software and model artifacts.

---- Basic Qualifications ----

1. Master or PhD or equivalent experience in Computer Science, Engineering, Mathematics or a related field and 2+ years of Software Engineering work experience.
2. Experience in programming with a language such as Python, C, C++, Java, or Go.
3. Experience with ML packages such as Tensorflow, PyTorch, JAX, and Scikit-Learn.
4. Experience with SQL and database systems such as Hive, Kafka, and Cassandra.
5. Experience in the development, training, productionization and monitoring of ML solutions at scale.
6. Strong desire for continuous learning and professional growth, coupled with a commitment to developing best-in-class systems.
7. Excellent problem-solving and analytical abilities.
8. Proven ability to collaborate effectively as a team player.

---- Preferred Qualifications ----

1. Prior experience working with generative AI (e.g., LLMs, diffusion models) or multimodal AI and integrating such technologies into end-user products.
2. Exposure to audio ML and voice AI (STT, TTS, voice embeddings, etc.).
3. Experience in modern deep learning architectures and probabilistic models.
4. Machine Learning, Computer Science, Statistics, or a related field with research or applied focus on large-scale ML systems.

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.

Client-provided location(s): San Francisco, CA

Job ID: Uber-146991

Employment Type: FULL_TIME

Posted: 2025-08-27T00:27:59