Staff Machine Learning Engineer, Delivery Matching

$218,000 - $242,000/Yr

Uber - San Francisco, CA

Full-time - Senior

San Francisco, CA

Transit and Ground Passenger Transportation

About the position

The Staff Machine Learning Engineer for Delivery Matching at Uber plays a crucial role in the Delivery Marketplace, which is essential for the company's delivery products. This position involves leading the development and productionization of optimization solutions that utilize real-time and machine learning signals to address strategically important challenges. The role requires collaboration with product managers, data scientists, and engineers to enhance the efficiency of the delivery marketplace while ensuring an exceptional user experience.

Responsibilities

• Lead the design, development, optimization, and productization of machine learning (ML) solutions and systems.
• Build ML solutions to improve Delivery marketplace efficiency while delivering magical user experience.
• Lead ML engineers, providing technical leadership and vision for the team.

Requirements

• PhD or equivalent experience in Computer Science, Engineering, Mathematics or a related field.
• 6 years of Software Engineering work experience.
• Experience in programming with languages such as Python, C, C++, Java, or Go.
• Experience with ML packages such as Tensorflow, PyTorch, JAX, and Scikit-Learn.
• Experience with SQL and database systems such as Hive, Kafka, and Cassandra.
• Experience in the development, training, productionization, and monitoring of ML solutions at scale.

Nice-to-haves

• Experience in a technical leadership role and mentoring junior engineers.
• Experience in modern deep learning architectures and probabilistic models.
• Experience in optimization (RL / Bayes / Bandits) and online learning.
• Experience in causal inference/personalization/ranking.

Benefits

• Participation in Uber's bonus program.
• Eligibility for equity awards and other types of compensation.
• Various benefits as detailed on Uber's careers page.