Machine Learning Engineer

$158,000 - $175,500/Yr

Uber - San Francisco, CA

Full-time - Mid Level

Remote - San Francisco, CA

Transit and Ground Passenger Transportation

About the position

The Machine Learning Engineer role at Uber's Investment Modeling Team focuses on developing and implementing ML-driven algorithms to enhance the company's pricing and incentive strategies across its global operations. The team utilizes advanced machine learning and optimization techniques to analyze large datasets, estimate pricing impacts, and identify optimal investment strategies. The position involves building and deploying ML models, collaborating with cross-functional teams, and contributing to the development of in-house ML infrastructure.

Responsibilities

• Design and build Machine Learning models with optimization engines.
• Productionize and deploy these models for real-world application.
• Review code and designs of teammates, providing constructive feedback.
• Collaborate with Product and cross-functional teams to brainstorm new solutions and iterate on the product.

Requirements

• Bachelor's degree or equivalent in Computer Science, Engineering, Mathematics or related field, with 2+ years of full-time engineering experience or PhD new grad.
• Experience working with multiple multi-functional teams (product, science, product ops etc).
• Expertise in one or more object-oriented programming languages (e.g. Python, Go, Java, C++).

Nice-to-haves

• 1+ year of ML experience and building ML models.
• Experience with the design and architecture of ML systems and workflows.
• Experience with building algorithmic solutions in production, making practical tradeoffs among algorithm sophistication, compute complexity, maintainability, and extensibility in production environments.
• Experience with taking on vague business problems, translating them into ML + Optimization formulation, identifying the right features, model structure and optimization constraints, and delivering business impact.
• Experience with optimizing Spark queries for better CPU and memory efficiency.
• Working knowledge of latest ML technologies, and libraries, such as PyTorch, TensorFlow, Ray, etc.
• Experience owning and delivering a technically challenging, multi-quarter project end to end.
• Experience with big-data architecture, ETL frameworks and platforms, such as HDFS, Hive, MapReduce, Spark, etc.

Benefits

• Participation in Uber's bonus program.
• Potential equity award and other types of compensation.
• Various benefits as detailed in the company policy.