Senior Machine Learning Engineer| Direct

About the Role

Uber Direct powers fast, reliable delivery for enterprise retailers and local businesses by leveraging Uber's world-class logistics network. As a Senior Machine Learning Engineer on the Uber Direct team, you will define and build intelligent systems that improve operational efficiency, customer experience, and predictive capabilities in real‑time logistics at global scale.

• Design, build, and product ionize machine learning models that solve critical logistics problems such as ETA prediction, demand forecasting, dispatch optimization, anomaly detection, and delivery quality improvements.
• Lead projects from problem definition and data exploration through feature engineering, model development, evaluation, deployment, monitoring, and iteration.
• Develop robust data pipelines, feature stores, training workflows, and model serving infrastructure that support both real‑time and batch inference at scale.
• Define success metrics, run experiments, and rigorously evaluate model performance to ensure measurable improvements to KPIs such as Completion Rate, On‑Time Rate, and Defect Rate.
• Work closely with Product Managers, Data Scientists, Operations, and Backend Engineers to translate business problems into scalable ML solutions.
• Provide technical direction, establish best practices in ML and MLOps, and mentor engineers across the team.

• Bachelor’s degree in Computer Science, Machine Learning, Statistics, Mathematics, or a related technical field, or equivalent practical experience.
• 5+ years of experience building and shipping production‑grade machine learning systems.
• Strong proficiency in Python and experience with at least one additional programming language (e.g., Go, Java, C++, Scala).
• Hands‑on experience with modern ML frameworks such as PyTorch, Tensor Flow, JAX, or Scikit‑Learn.
• Demonstrated experience deploying, monitoring, and maintaining ML models in production environments.
• Solid understanding of statistics, feature engineering, model evaluation methodologies, and experimental design.
• Strong software engineering fundamentals, including data structures, algorithms, and system design.

• Master’s or PhD in Machine Learning, Computer Science, Statistics, or related field.
• Experience building large‑scale ML systems in a high‑throughput, low‑latency production environment.
• Background in logistics, marketplace systems, forecasting, optimization, recommendation systems, or time‑series modeling.
• Experience with distributed data processing frameworks (e.g., Spark, Hive) and streaming systems (e.g., Kafka).
• Familiarity with MLOps tooling such as Airflow, Kubeflow, MLflow, feature stores, and CI/CD pipelines for ML workflows.
• Experience with A/B testing, experimentation frameworks, and causal inference.
• Proven ability to optimize ML systems for scalability, reliability, observability, and latency.
• Experience mentoring engineers and contributing to technical strategy.

• Strong foundation in ML theory and applied modeling, with the ability to balance trade‑offs between accuracy, interpretability, and system performance.
• Ability to design and implement scalable, maintainable ML systems that operate reliably in production.
• End‑to‑end accountability for model quality, system health, and business impact.
• Ability to influence and collaborate effectively with Product, Science, and Engineering stakeholders.
• Focus on delivering measurable improvements to core business metrics through data‑driven solutions.

At Uber Direct, you’ll help shape the future of logistics through data‑driven intelligence at global scale. Your work will directly power the technology behind enterprise delivery and impact millions of customers worldwide. Join a team where experimentation, innovation, and ownership are core to our engineering culture.

Salary:
For New York, NY–based roles: USD $202,000–224,000 per year. For San Francisco, CA–based roles: USD $202,000–224,000 per year. For all US locations, you will be eligible to participate in Uber’s bonus program, may receive an equity award, and have access to a 401(k) plan and additional benefits.