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 algorithms, RAG pipelines, or large-scale content indexing, along with strong proficiency in at least one modern programming language (e.g., Python, Java, Go, or C++).
3. Demonstrated experience building end-to-end ML systems at scale, from offline experimentation and evaluation to online deployment, monitoring, and feedback loops, ideally in a customer-facing or platform environment.

Bonus Points If:

1. Strong experience building and operating ML-powered platforms at scale**, including observability, evaluation frameworks, and production monitoring for model quality, latency and failure modes.**2. Deep expertise in information retrieval and relevance optimization, such as semantic search, retrieval-augmented generation (RAG), ranking algorithms, embeddings, and coverage analysis across large, evolving content corpora.
2. Proven ability to drive end-to-end technical solutions, from experimentation and algorithm design to production systems, with experience partnering cross-functionality (e.g. content, Nova, Evals, product and infra teams) to close feedback loops and deliver measurable impact.

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 fuelds progress. What moves us, moves the world - let's move it forward, together.

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.

Accommodations may be available based on religious and/or medical conditions, or as required by applicable law. To request an accommodation, please reach out to accommodations@uber.com.