About the role and team
As a Senior Localization Operations Program Manager for ML and GenAI, you will lead the evolution of our multilingual operations, transforming how Uber speaks to millions of users across the globe. This isn't just about managing translations; it's about building the production-grade infrastructure that integrates Machine Translation and Large Language Models into the very fabric of our global platform.
The work is fast-paced, highly technical, and often ambiguous as we navigate the cutting edge of AI-driven language systems. You will need to balance the \'perfect\' with the \'practical,\' making critical trade-offs between quality, latency, and cost in a high-stakes environment. We are looking for a systems-thinker who is energized by the challenge of moving experimental capabilities into reliable production workflows and who possesses the grit to unblock complex cross-functional hurdles. If you thrive on owning end-to-end impact and want to shape the future of global communication at scale, this is where you'll grow.
What you'll do
- Design, build, and scale end-to-end localization workflows that integrate CMS, TMS, and ML/LLM systems to support Uber's global movement.
- Evaluate and operationalize MT and LLM solutions across high and low-resource languages, leveraging GCP services like Vertex AI and BigQuery to balance performance with cost.
- Partner closely with Engineering and MLOps to translate experimental AI capabilities into production-grade systems, including model hosting, monitoring, and lifecycle management.
- Navigate complex trade-offs between translation quality, turnaround time, and infrastructure costs, providing data-driven recommendations to leadership.
- Lead cross-functional initiatives across global time zones, aligning Product, Data, and Vendor teams to ensure seamless execution and guardrail implementation.
- Drive continuous improvement by establishing quality frameworks that combine automated metrics like COMET or BLEU with structured human evaluation.
Basic Qualifications
- Bachelor's degree in Computer Science, Engineering, Linguistics, or a related field; or 4 years of equivalent professional experience.
- 4+ years of experience in localization operations (CMS/TMS) within a technology or platform environment.
- 2+ years of experience working directly with MT, LLM, or ML-driven language systems in a production setting.
- Experience managing end-to-end technical programs, including scoping, risk management, and cross-functional stakeholder alignment.
- Working knowledge of Google Cloud Platform (GCP) or similar cloud services, including Vertex AI, AutoML, or BigQuery.
Preferred Qualifications
- 3+ years of experience leading large-scale MT/LLM transformation programs.
- Hands-on experience fine-tuning LLMs for multilingual use cases or implementing RAG pipelines for grounded translation.
- Proficiency in Python scripting or API integration to automate data flows and reduce operational overhead.
- Demonstrated ability to build data dashboards that track model performance, throughput, and cost-per-word metrics.
- Proven track record of navigating high-ambiguity environments and building frameworks for unique technical challenges.
For Sunnyvale, CA-based roles: The base salary range for this role is USD$167,000 per year - USD$185,500 per year.
You will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits. More details can be found at the following link [https://jobs.uber.com/en/benefits](https://jobs.uber.com/en/benefits).
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.
