The Ads Machine Learning team at Uber is responsible for designing, building, and evolving the core ML systems that powers ads selection, ranking, pricing, and delivery across the Uber ecosystem. We develop a deep understanding of user intent and merchant objectives to produce high quality ML signals that drive large scale auction based decision making. These systems operate under strict latency, reliability, and fairness constraints while serving billions of predictions that directly impact user experience, advertiser performance, and revenue outcomes.
As a Staff Machine Learning Engineer, you will play a central role in defining and executing the Ads ML technical roadmap. You will lead the design of next generation recommendation and auction architectures, enable step function improvements in model quality and serving efficiency, and raise the bar on observability and reliability of online ML systems. This role requires end to end ownership across modeling, training, online inference, and system integration, as well as close collaboration with product, infrastructure, and platform teams. Delivering robust, scalable, and measurable ad recommendations is critical to Uber’s rapidly growing Ads business, making this a highly visible and high impact role.
What the Candidate Will Do:
- Lead the design and evolution of machine learning models that power ads ranking, pricing, and auction systems at scale.
- Own end to end ML systems, including training pipelines, feature infrastructure, and low latency online inference for real time and batch use cases.
- Apply advanced statistical and ML techniques to improve ads relevance, marketplace efficiency, and advertiser outcomes.
- Define experimentation strategies, success metrics, and evaluation frameworks, and drive iteration through rigorous offline and online testing.
- Establish model and system observability through metrics, dashboards, and reliability best practices.
- Translate ambiguous product goals into durable ML architectures in close partnership with Product and Engineering.
- Provide technical leadership through mentorship, design reviews, and raising engineering standards across the Ads ML org.
- Stay current on advances in machine learning and ads auction systems, and drive adoption where they deliver clear impact.
Basic Qualifications:
- Bachelor’s degree or equivalent experience in Computer Science, Computer Engineering, Data Science, Machine Learning, Statistics, or a related quantitative field.
- Demonstrated ownership of designing, deploying, and evolving large scale machine learning systems powering ads ranking, auction, or pricing in production environments.
- Strong proficiency in Python for building production ML systems and defining model, feature, and training abstractions used across teams.
- Deep understanding of SQL with experience driving production decision making, data validation, and system level analysis.
- Strong grasp of big data and distributed system architectures, with experience designing data platforms and ETL pipelines that support Ads ML workloads.
- Hands on experience building and operating batch data pipelines using Spark or comparable distributed compute frameworks, with accountability for data quality and correctness.
- Proven expertise in experimentation and evaluation, including A/B testing and offline metrics for ads auctions, ranking quality, and marketplace outcomes.
- Experience defining and operationalizing model and serving level metrics, and building observability for reliable online ML inference systems.
- Experience owning or influencing online model serving, including latency aware inference, scalability, and reliability considerations.
- Strong grounding in statistical methods, with the ability to reason about bias, uncertainty, and tradeoffs in ads and marketplace systems.
- Demonstrated ability to influence product and technical direction by synthesizing complex modeling insights into clear recommendations.
- Ability to operate independently in ambiguous problem spaces, set technical direction, and drive alignment across ML, product, and platform teams.
- Strong communication skills across technical and executive audiences, with a consistent track record of mentorship and feedback.
Preferred Qualifications :
- 7 or more years of industry experience as a Machine Learning Engineer or equivalent, with demonstrated impact at Staff or equivalent scope.
- Proven experience leading large, ambiguous technical initiatives and setting direction across teams in fast moving, cross functional environments.
- Experience designing, scaling, and operating production ML systems end to end, including training, deployment, and online inference.
- Hands on experience with online model serving and inference optimization, including latency aware systems, GPU based serving, or platforms such as Triton.
- Direct experience building or evolving ads auction systems, including ranking, pricing, calibration, or marketplace tradeoffs.
- Experience applying state of the art deep learning architectures for large scale recommendation or ranking systems, including modern GenRec patterns.
- Advanced degree (M.S. or Ph.D.) in Machine Learning, Data Science, or a related field is a plus.
For New York, NY-based roles: The base hourly rate amount for this role is USD$30.00 per hour.
You will also be eligible for various benefits.
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.
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.