About The Role
The
Offers team's mission is to enhance Uber's offer capabilities and marketplace positioning by building personalized, strategic promotions that align with merchant and consumer needs.
The team works on projects that increase offer redemption and business growth, such as improving offer-quality models, enabling dynamic pricing, and integrating advanced machine learning models to refine offer recommendations.
As a
Sr ML/AI engineer, the candidate would shape and scale these core models and decision systems, directly improving offer efficiency and personalization, and in turn driving customer engagement, sales, and retention across Uber's delivery businesses
What You'll Do
- Design, build, and productionize ML models (e.g., ranking, personalization, deep learning/GenAI) that solve core business problems and directly move key metrics.
- Own the end-to-end ML lifecycle - from problem formulation and data/feature pipelines to training, evaluation, deployment, and monitoring in high-traffic, low-latency production systems.
- Run rigorous experimentation (A/B tests, offline/online evals), define success metrics, and iterate quickly based on data to refine models and policies.
- Collaborate cross-functionally with Product, Data Science, and Engineering to translate ambiguous business needs into ML roadmaps and influence product strategy with algorithmic insights.
- Raise the technical bar by leading design and code reviews, mentoring junior engineers, and improving ML infrastructure, observability, and best practices for the broader team
What You'll Need
- Deep ML & domain expertise: 6+ years of experience building state-of-the-art models (e.g., deep learning, ranking/recommendation, causal/RL, or GenAI) with a track record of materially improving key business metrics in production.
- Large-scale systems & infra: Hands-on ownership of end-to-end ML pipelines-from data and features (Spark/Hive/Presto) to training, evaluation, and low-latency online serving handling millions of predictions per second, with strong MLOps and observability practices.
- Product + experimentation mindset: Experience turning ambiguous product problems into ML formulations, designing objective functions, running A/B experiments, and iterating quickly to deliver sustained business impact across multiple quarters.
- Technical leadership & collaboration: Proven ability to set technical direction, mentor other engineers, and drive cross-functional projects with Product, DS, and Ops-owning architectural decisions, code quality, and long-term reliability of critical ML systems.
- PhD or Master's (or strong Bachelor's) in Computer Science, Machine Learning, or a related quantitative field, with experience building ML/AI systems in industry.
- Proven track record of designing, training, and productionizing large-scale ML models (e.g., ranking/recommendation, personalization, or deep learning/GenAI systems) including experimentation, monitoring, and iterative improvement in high-traffic environments.
- Strong coding skills in Python plus at least one of Java/Go (or similar)
- Experience working cross-functionally with product, data science, and engineering partners to translate ambiguous problems into high-impact ML solutions.
