Senior Machine Learning Engineer| Uber 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.

You'll partner closely with Product, Data Science, and Engineering teams to design, deploy, and continually enhance machine learning-driven solutions that power core decision-making across the delivery lifecycle. Your work will directly influence key marketplace and logistics metrics across millions of global deliveries.

What You'll Do

• Develop High-Impact ML Solutions: Design, build, and productionize machine learning models that solve critical logistics problems such as ETA prediction, demand forecasting, dispatch optimization, anomaly detection, and delivery quality improvements.
• Own the End-to-End ML Lifecycle: Lead projects from problem definition and data exploration through feature engineering, model development, evaluation, deployment, monitoring, and iteration.
• Build Scalable ML Systems: Develop robust data pipelines, feature stores, training workflows, and model serving infrastructure that support both real-time and batch inference at scale.
• Drive Business Impact: 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.
• Collaborate Cross-Functionally: Work closely with Product Managers, Data Scientists, Operations, and Backend Engineers to translate business problems into scalable ML solutions.
• Technical Leadership & Mentorship: Provide technical direction, establish best practices in ML and MLOps, and mentor engineers across the team.
  
  

Basic Qualifications

• 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 , plus experience with at least one additional programming language (e.g., Go, Java, C , Scala).
• Hands-on experience with modern ML frameworks such as PyTorch, TensorFlow, 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.
  
  

Preferred Qualifications

• 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.
  
  

Success Attributes

Machine Learning Depth: Strong foundation in ML theory and applied modeling, with the ability to balance trade-offs between accuracy, interpretability, and system performance.

Engineering Excellence: Ability to design and implement scalable, maintainable ML systems that operate reliably in production.

Ownership Mindset: End-to-end accountability for model quality, system health, and business impact.

Cross-Functional Leadership: Ability to influence and collaborate effectively with Product, Science, and Engineering stakeholders.

Impact Orientation: Focus on delivering measurable improvements to core business metrics through data-driven solutions.

Why Uber Direct?

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.

For Seattle, WA-based roles: The base salary range for this role is USD$202,000 per year - USD$224,000 per year. You will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits. More details can be found at the following link https://jobs.uber.com/en/benefits., For Seattle, WA-based roles: The base salary range for this role is USD$202,000 per year - USD$224,000 per year. You will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits. More details can be found at the following link https://jobs.uber.com/en/benefits.