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# Staff Technical Program Manager

- **Company:** PlayStation
- **Location:** Austin, TX, United States
- **Experience:** Experienced
- **Salary:** $206,800.0 - $310,200.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Information Engineering, Distributed Systems, Issue Tracking Systems, Machine Learning, Scrum Methodology, Software Engineering, Feature Store, Information Technology, Data Analytics, Machine Learning Operations
- **Published:** November 4, 2026
- **Apply:** https://job-boards.greenhouse.io/sonyinteractiveentertainmentglobal/jobs/6242628004

## About the Role

* 7+ years as a technical program manager, with at least 3 years specifically on 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, can understand why a deployment might be blocked on data quality vs. infra vs. model performance, and ask the right question in each case
 * 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 day-to-day execution for agile/scrum teams, including sprint planning, backlog health, and velocity tracking
 * Strong written and verbal communication across both highly technical and executive/non-technical audiences, * Gaming Experience is a Plus
 * Direct experience in the specific AI/ML domain named for this role (e.g, trust & safety, personalization, recommendation, experimentation).
 * Hands-on software development background
 * Experience with ML infrastructure tooling (feature stores, experimentation platforms, model registries, MLOps pipelines)
 * Prior work directly under or alongside engineering leadership
 * Demonstrated curiosity to continuing to learn new AI/ML techniques and tooling

#LI-KS1

## Description

* Own the delivery roadmap for one or more AI/ML systems in production, from model concept through training, deployment, and post-launch monitoring.
 * Define success with our business partners before delivery begins
 * Partner with applied scientists, Data Engineering, and ML Engineering to translate model performance targets (accuracy, latency, cost-per-inference) into a program plan with clear milestones and dependencies
 * Drive alignment across engineering, applied science, product, and data analytics on scope, tradeoffs, and sequencing when priorities are ambiguous or contested
 * Track model and system health post-launch, adoption, drift, cost, and incident rate - and drive remediation when metrics degrade, rather than treating launch as the finish line
 * Manage cross-team and cross-timezone dependencies for distributed system and ML teams, surfacing schedule risk early
 * Own vendor and third-party tooling relationships that intersect with ML delivery (labeling, compute, evaluation tooling), including production readiness and issue tracking
 * Communicate program status, risk, and tradeoffs in terms non-technical stakeholders can act on, and in terms technical stakeholders will trust
 * Identify and resolve conflicts between competing technical approaches or team priorities, escalating only what genuinely requires executive input.