Technical Program Manager, Data Collection and Model Evaluation (Fixed-Term Contract), DeepMind

Minimum qualifications:

• Bachelor’s degree in a technical, quantitative, or operational field, or equivalent practical experience.
• 5 years of experience in technical program management, data operations, or AI/ML program management.

Preferred qualifications:

• Manage end-to-end human data collection, annotation, and evaluation pipelines (SFT, RLHF) for large-scale foundation models.
• Oversee third-party vendors, SOWs, and rater quality, partnering with engineering to design and launch workflows on annotation platforms.
• Utilize SQL, Python, or dashboards to monitor pipeline telemetry, analyze rater quality, and diagnose data anomalies.
• Navigate ambiguity to build consensus across research, engineering, and legal, managing multimodal and internationalized data workflows.

About the job:

A problem isn’t truly solved until it’s solved for all. That’s why Googlers build products that help create opportunities for everyone, whether down the street or across the globe. As a Technical Program Manager at Google, you’ll use your technical expertise to lead complex, multi-disciplinary projects from start to finish. You’ll work with stakeholders to plan requirements, identify risks, manage project schedules, and communicate clearly with cross-functional partners across the company. You're equally comfortable explaining your team's analyses and recommendations to executives as you are discussing the technical tradeoffs in product development with engineers.

We are seeking a Technical Program Manager (12-month Fixed-Term Contract) to lead complex data collection and model evaluation programs. Sit at the intersection of research, engineering, and vendor operations, you will translate research requirements into scalable pipelines across modalities. You will autonomously oversee the program lifecycle for pre-training and fine-tuning datasets, managing experimental pilot design, rater rubrics, vendor execution, quality auditing, and final delivery.

The DeepMind Data Programs and Operations team scales high-quality data powering Google's next-generation models. Within this organization, the capability expansion and platform pillar builds the foundational infrastructure and execution recipes that turn novel research into production-grade training data. Joining this team puts you at the forefront of GenAI innovation, solving pioneering data challenges that define Google's flagship models.

Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.

We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $163000 - $236000 (USD) + 15% bonus target

Learn more about benefits at Google.

Responsibilities:

• Manage complex human data collection and model evaluation programs spanning pre-training, SFT, and benchmark evaluations across multimodal domains from requirement intake through final data handoff.
• Partner with research and engineering to translate modeling goals into concrete data specifications, annotation guidelines, and evaluation rubrics, while driving workflow migrations onto internal platforms.
• Coordinate technical dependencies for novel data collection environments (e.g., virtualized OS, multimodal capture), resolving tooling bottlenecks and ingestion issues.
• Oversee external data service providers and rater pools, managing SOWs, capacity, throughput SLAs, and quality audits to ensure datasets meet research quality bars.
• Establish program tracking and operational metrics; clearly communicate progress, risk mitigation plans, and resource status to cross-functional partners and leadership.