Uber Machine Learning Engineer II - Applied AI 

United States, West Virginia 

396541292

About the Role

- - - - 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.