Staff Technical Program Manager - AI/ML

PlayStation•Austin, TX

21h

About The Position

Sony Interactive Entertainment (SIE), the company behind the PlayStation brand, is seeking a Staff Technical Program Manager for its AI/ML Engineering organization in Austin, Texas. The organization develops and operates large-scale machine learning systems that support PlayStation experiences, systems, and products for a global user base. Its work spans applied science, AI, ML infrastructure, and engineering, including designing, training, deploying, and continuously evaluating production systems. The role owns delivery roadmaps for production AI/ML systems and coordinates work across Engineering, Product, Design, Applied Science, Data Engineering, ML Engineering, and Data Analytics. The program manager translates model and system objectives into plans, aligns stakeholders on scope and tradeoffs, manages dependencies and risks, and monitors systems after launch. The position also involves third-party tooling and vendor relationships, communicating program status to technical and non-technical audiences, and resolving conflicts among technical approaches or team priorities.

Requirements

At least 7 years of experience as a technical program manager, including at least 3 years specifically in AI/ML or applied data science delivery.
Bachelor's degree in Computer Science, Engineering, or a related technical field.
Working fluency in the ML lifecycle, including the ability to distinguish potential data quality, infrastructure, and model performance deployment blockers.
Track record of running distributed, cross-timezone engineering programs end-to-end.
Demonstrated ability to drive decisions among senior technical stakeholders without formal authority.
Experience managing agile/scrum team execution, including sprint planning, backlog health, and velocity tracking.
Strong written and verbal communication skills for technical, executive, and non-technical audiences.

Nice To Haves

Gaming experience.
Direct experience in the AI/ML domain relevant to the role, such as trust and safety, personalization, recommendation, or experimentation.
Hands-on software development background.
Experience with ML infrastructure tooling, such as feature stores, experimentation platforms, model registries, or MLOps pipelines.
Prior work directly under or alongside engineering leadership.
Demonstrated curiosity and commitment to continuing to learn new AI/ML techniques and tooling.

Responsibilities

Own delivery roadmaps for one or more production AI/ML systems, from model concept through training, deployment, and post-launch monitoring.
Define success with business partners before delivery begins.
Work with applied scientists, Data Engineering, and ML Engineering to translate model performance targets, including accuracy, latency, and cost-per-inference, into program plans with milestones and dependencies.
Align Engineering, Applied Science, Product, and Data Analytics stakeholders on scope, tradeoffs, and sequencing.
Monitor post-launch model and system health, adoption, drift, cost, and incident rate, and drive remediation when metrics degrade.
Manage cross-team and cross-timezone dependencies for distributed systems and ML teams, surfacing schedule risks early.
Manage vendor and third-party tooling relationships that intersect with ML delivery, including labeling, compute, and evaluation tooling, and track production readiness and issues.
Communicate program status, risks, and tradeoffs to technical and non-technical stakeholders.
Resolve conflicts between competing technical approaches or team priorities, escalating issues that require executive input.
Manage day-to-day execution for agile/scrum teams, including sprint planning, backlog health, and velocity tracking.