Sr. ML Engineer

Sr. ML Engineer Department: Engineering Team: Machine Learning Location: Bangalore, India Type: Full-Time **About the Role** As a Senior ML Engineer on the Content Platform team, you will help build the core machine-learning capabilities that power how millions of customers interact with Nova (our customer support chat bot). The team's mission is to deeply understand, measure, and improve the quality of bot responses through robust observability, innovative retrieval and ranking algorithms, and intelligent content coverage strategies. You will work on industry-scale, technically challenging problems at the intersection of ML systems, information retrieval, and platform engineering. A key part of the role is closing the loop between user behavior and content authors, enabling proactive improvements through data-driven feedback. Your work will directly shape response relevance, trust, and customer experience at Uber's scale. **What the Candidate Will Do :** 1. Define and implement observability and evalution frameworks to measure response quality, relevance, coverage gaps, latency, and failure modes across customer interactions. 2. Develop and iterate on advanced retrieval, ranking, and coverage algorithms (e.g. semantic search, RAG improvements, content expansion strategies) to continuously improve answer relevance. 3. Build automated feedback loops that surface insights from customer queries back to content authors and partner teams, enabling proactive identification and resolution of coverage issues. 4. Collaborate closely with product, ML, infra, and content stakeholders to translate ambiguous problem spaces into measurable improvements and production-ready systems with real customer impact. **What the Candidate Will Need :** 1. 5+ years of professional software engineering experience, with atleast 3+ years working on machine-learning or information-retrieval systems in production, including ownership of reliability, observability, and quality metrics. 2. Hands-on experience with retrieval and relevance technologies, such as semantic search, embeddings, ranking a