Machine Learning Engineer - Applied AI

Uber - San Francisco, CA

Full-time - Mid Level

San Francisco, CA

Transit and Ground Passenger Transportation

About the position

The Applied AI team at Uber is at the forefront of developing innovative AI solutions that address core business challenges. This team collaborates closely with various product teams to identify and understand these challenges, exploring the potential of AI to provide effective solutions. The focus areas of expertise within the team include Computer Vision, ML Optimization, Geospatial AI, Personalization, and Generative AI. Within this framework, the Generative AI team is dedicated to creating a semantic layer that enhances our comprehension of entities pertinent to the Uber platform, such as places, merchants, items, riders, and eaters. This involves working in partnership with other teams to design, develop, and productionize semantic data, features, and embeddings that align with business requirements. The role is pivotal in ensuring that AI solutions are not only innovative but also practical and applicable to real-world scenarios. As a Machine Learning Engineer in this team, you will be responsible for building and iterating on methods to capture semantic information related to Uber entities by utilizing Large Language Models (LLMs). You will generate embeddings from this semantic information, which will play a crucial role in enhancing our understanding of various entities, ultimately leading to improved machine learning models across Uber. Additionally, your work will contribute to the development of novel personalized experiences for users, leveraging the insights gained from the semantic data.

Responsibilities

• Build and iterate on capturing semantic information of Uber entities by leveraging LLMs.
• Generate embeddings using the semantic information to help improve our understanding of places, merchants, items, and users.
• Leverage semantic information to improve ML models across Uber and to build novel personalized experiences.

Requirements

• Strong background in machine learning and AI, particularly in the areas of natural language processing and computer vision.
• Experience with large language models (LLMs) and their applications in real-world scenarios.
• Proficiency in programming languages such as Python, and familiarity with ML frameworks like TensorFlow or PyTorch.
• Ability to collaborate effectively with cross-functional teams, including engineering, product, and data science.

Nice-to-haves

• Experience in geospatial AI and its applications in business solutions.
• Familiarity with personalization techniques and their implementation in machine learning models.
• Knowledge of generative AI techniques and their practical applications.

Benefits

• Competitive salary and performance-based bonuses.
• Comprehensive health insurance plans including medical, dental, and vision coverage.
• 401(k) retirement savings plan with company matching contributions.
• Flexible work hours and remote work options.
• Generous paid time off and holidays.