Sr ML Engineer

Sr ML Engineer Department: Engineering Team: Machine Learning Location: Bangalore, India Type: Full-Time **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:** 1. **Design, build, and productionize** ML models (e.g., ranking, personalization, deep learning/GenAI) that solve core business problems and directly move key metrics. 2. **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. 3. **Run rigorous experimentation** (A/B tests, offline/online evals), define success metrics, and iterate quickly based on data to refine models and policies. 4. **Collaborate cross-functionally** with Product, Data Science, and Engineering to translate ambiguous business needs into ML roadmaps and influence product strategy with algorithmic insights. 5. **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:** 1. **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. 2. **Large-scale systems & infra:** Hands-on ownership of **end-to-end ML pipelines**—from data and features (Spark