below marketmost similar roles pay hereabove market
This listing doesn't post a salary. Most similar roles pay
$202,800–$254,750.
Based on 240 similar postings.
Employer
About Uber
Uber Technologies, Inc. is the world’s largest, San Francisco-based mobile technology platform facilitating on-demand ride-hailing, food delivery (Uber Eats), and freight transportation across approximately 70 countries.
The Machine Learning Engineer II, Pricing joins the Dynamic Supply Pricing team to develop models, algorithms, and large-scale distributed systems for real-time driver pricing. In this role, you will lead the design, development, and productionization of advanced ML solutions and pricing algorithms for multi-sided real-time marketplaces. You will build end-to-end solutions for large-scale distributed systems, combining machine learning, algorithmic game theory, and optimization to provide earnings opportunities for millions of drivers. Required skills include proficiency in Python, Scala, Java, or Go, along with experience in Spark, Ray, Flink, and microservices architectures. You will utilize deep learning, causal modeling, reinforcement learning, DNNs, transformers, and mathematical optimization like LP and convex optimization to solve complex marketplace problems and deliver high-impact technical solutions.
What does a Machine Learning Engineer earn in New York?
Median $231150 from 51 postings across 20 companies.
Design, develop, and productionize end-to-end ML solutions for large-scale distributed systems.
Develop novel pricing approaches using machine learning, algorithmic game theory, and optimization.
Build models for real-time multi-objective optimizations serving over 1 million predictions per second.
Implement deep learning, causal modeling, and reinforcement learning techniques for marketplace problems.
Translate ambiguous business problems into structured technical solutions and high-impact designs.
Manage the full ML lifecycle from offline pipelines to online serving and MLOps monitoring.
Plan project scope and execution while mentoring junior team members on design and implementation.
What we're looking for
Ph.D., M.S., or Bachelor's degree in Computer Science, Machine Learning, or Operations Research, or an equivalent technical background with exceptional demonstrated impact.
2+ years of experience developing and deploying machine learning models and optimization algorithms in large-scale production environments.
Proficiency in programming languages such as Python, Scala, Java, or Go.
Experience with large-scale data systems (e.g. Spark, Ray), real-time processing (e.g. Flink), and microservices architectures.
Experience in the development, training, productionization, and monitoring of ML solutions at scale, from offline pipelines to online serving and MLOps.
Familiarity with modern ML algorithms (e.g. DNNs, multi-task models, transformers) and mathematical optimization (e.g. LP, convex optimization).
Experience in translating ambiguous business problems into technical solutions in a structured and principled way (preferred).
Experience in developing and deploying pricing algorithms for multi-sided real-time marketplaces with strategic agent behavior (preferred).
Python
PyTorch
C++
Linux
Machine Learning
Computer Vision
Robotics
High-Performance Computing
System Design
Algorithm Development
Data Mining
Auto-labeling
Physical AI
Causal Modeling
Machine Learning
Deep Learning
Generative AI
Optimization Algorithms
Python
C++
Java
Go
C
Spark
Hive
Causal Modeling
Reinforcement Learning
Representation Learning
Large-scale Data Systems
Production-ready Algorithmic Systems
Machine Learning
Deep Learning
Generative AI
Optimization Algorithms
Python
C++
Java
Go
C
Spark
Hive
Causal Modeling
Reinforcement Learning
Representation Learning
Large-scale Data Systems
Production-ready Algorithmic Systems