Engineering Program Manager (Data & AI Operations) | Contract

Summary

We are hiring an Engineering Program Manager (EPM) to join the Human-Centered AI (HAI) EPM team, supporting the HAI Data Operations team.
This role will drive programs at the intersection of data operations, data engineering, and internal tooling that enable the development and evaluation of Generative AI (GenAI) and machine learning systems. The EPM will partner closely with engineering, data operations, research, and cross-functional teams to define roadmaps, prioritize investments, improve tooling and workflows, and drive complex programs from planning through execution.
The ideal candidate has a background in technical program management, data management, data engineering, and tooling. They do not need to be a software engineer, but should have sufficient technical depth to engage effectively with engineers on system architecture, data pipelines, APIs, technical dependencies, and engineering tradeoffs.
This role requires good communication, prioritization, and cross-functional leadership skills and will work primarily Pacific Time (PST/PDT) hours. 

Key Responsibilities

• Drive end-to-end program execution for HAI Data Operations, including roadmap development, prioritization, planning, milestone tracking, dependency management, and risk mitigation.
• Partner with Data Operations and Engineering leadership to build and maintain a clear technical roadmap, balancing operational needs, tooling investments, engineering capacity, and broader HAI priorities.
• Lead prioritization across competing data operations and tooling requirements, helping teams make clear tradeoffs and focus engineering resources on the highest-impact work.
• Drive programs focused on improving and scaling data management, data pipelines, data processing, and operational tooling supporting AI/ML development and evaluation.
• Partner closely with engineers to define requirements for internal data and developer tooling, translating operational needs and pain points into clear technical requirements and execution plans.
• Develop a good understanding of the team's technical architecture, systems, APIs, data flows, and tooling to identify dependencies, risks, bottlenecks, and opportunities for improvement.
• Facilitate technical discussions and architecture reviews, ensuring decisions, dependencies, tradeoffs, and follow-up actions are clearly understood and driven to closure.
• Work with engineering teams to improve the reliability, scalability, efficiency, and usability of data operations workflows and supporting platforms.
• Identify opportunities to automate and streamline manual or repetitive data operations processes through improved tooling and workflows.
• Coordinate across engineering, research, data science, and other HAI teams to ensure data and tooling capabilities meet the needs of GenAI development and evaluation programs.
• Establish clear program metrics and mechanisms to measure operational efficiency, tooling adoption, reliability, throughput, and overall program health.
• Communicate program status, technical risks, dependencies, tradeoffs, and decisions clearly to both technical teams and leadership.
• Create scalable program management processes that improve visibility, accountability, prioritization, and execution across Data Operations.

Minimum Qualifications

• Bachelor’s degree or equivalent experience in Computer Science, Engineering, Data Science, Information Systems, or a related technical field.
• 5+ years of experience in engineering program management, technical program management, product management, or a related role supporting complex engineering organizations.
• Experience managing programs involving data platforms, data engineering, data management, data operations, or large-scale data processing systems.
• Technical fluency and demonstrated ability to partner effectively with software and data engineers.
• Ability to understand and participate in discussions involving system architecture, data pipelines, APIs, services, technical dependencies, and engineering tradeoffs.
• Familiarity with software development concepts such as Python or Java sufficient to understand engineering discussions and technical designs; hands-on coding experience is not required.
• Experience driving tooling or platform programs, particularly internal tools used by engineering, data, or operations teams.
• Demonstrated experience developing and managing technical roadmaps and prioritization frameworks.
• Proven ability to drive complex, cross-functional programs with multiple dependencies and stakeholders.
• Ability to translate ambiguous operational needs into clear requirements, priorities, milestones, and execution plans.
• Excellent written and verbal communication skills, with the ability to communicate effectively across engineering teams, technical leadership, and senior stakeholders.
• Analytical and problem-solving skills with the ability to use data to inform prioritization and program decisions.
• Ability to work primarily Pacific Time (PST/PDT) hours.

Good to Have

• Experience supporting AI/ML or Generative AI development, evaluation, or data programs.
• Understanding of data pipelines, ETL/ELT workflows, data orchestration, dataset management, and large-scale data processing.
• Familiarity with technologies such as SQL, Spark, Airflow, Kubernetes, cloud data platforms, or similar data infrastructure technologies.
• Experience managing internal developer platforms, workflow automation, data tooling, or operational tooling.
• Understanding of the ML data lifecycle, including data collection, ingestion, transformation, curation, annotation, evaluation, and monitoring.
• Experience working with human data operations, annotation, labeling, or evaluation workflows.
• Familiarity with APIs, distributed systems, and service-oriented architectures.
• Experience defining operational metrics, SLAs/SLOs, dashboards, and mechanisms for measuring platform or workflow performance.
• Experience working in a fast-paced environment where priorities evolve based on research, product, and engineering needs.
• Experience supporting globally distributed engineering and operations teams.