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 [](;br>
Uber's mission is to reimagine the way the world moves for the better. Here, bold ideas create real-world impact, challenges drive growth, and speed fuels progress. What moves us, moves the world - let's move it forward, together.

Uber is proud to be an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing [this form](;br>
Offices continue to be central to collaboration and Uber's cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.