Senior Principal Engineer, Data & Machine Learning

sonyinteractiveentertainmentglobal; United States; On-site; USD 180,000 - 240,000; Full time

Job summary

Sony Interactive Entertainment's PlayStation Data & Intelligence group seeks a Senior Principal Engineer, Data & Machine Learning to serve as chief architect for our data platform. This is one of SIE's most senior individual-contributor roles, reporting to the VP and influencing decisions across the organization.

This hands-on role involves writing architectures, prototyping approaches, reviewing designs, and guiding trusted data, ML, and analytics practices across PlayStation.

Responsibilities

• Translate our multi-year Data & Intelligence strategy into a coherent technical strategy and target architecture, with clear principles for trusted data products, ML systems, shared intelligence, evaluation, activation and lifecycle management.
• Chair the architecture review forum: set its charter and cadence, publish decisions, track adoption and hold the line on non-conforming designs.
• Own the boundary decisions between platform capabilities built for reuse and domain delivery, and keep them stable.
• Define and ratify the standards teams build against: data contracts, golden paths, model evaluation and experimentation, observability, reliability, cost and l

Job description

Why Sony Interactive Entertainment?

Sony Interactive Entertainment isn't just the Best Place to Play - it's also the Best Place to Work. Sony Interactive Entertainment (SIE) is the company behind the PlayStation brand. As a subsidiary of Sony Group Corporation, we're part of a proud legacy of innovation and excellence. SIE is a dynamic technology company, delivering cutting-edge hardware and network services to more than 100 million people and an entertainment leader, home to some of the most beloved and recognizable intellectual properties (IP) in the world. Our role at SIE is to create and nurture the experiences under the PlayStation brand, a name synonymous with entertainment excellence and creativity.

Sr. Principal Engineer, Data & Machine Learning

San Mateo, CA

Why PlayStation Data & Intelligence

PlayStation reaches more than 100+ million people. Behind those experiences is one of the largest consumer data estates in entertainment: tens of petabytes under management and trillions of telemetry events a year, powering personalization, experimentation, partner analytics and enterprise decisions across the company.

We are in the middle of a multi-year shift in how that data creates value. Today intelligence often arrives after the moment has passed. Our goal is a connected intelligence layer where trusted context about players, content, partners and the business is built once, reused everywhere, activated at the point of decision, and improved by evidence about what happened next. That includes serving AI models and agents with the same governed meaning, quality and permissions we give to people.

This role helps shape how that is built across PlayStation.

The Role

We are looking for a Senior Principal Engineer, Data & Machine Learning to serve as chief architect for our Data & Intelligence organization and as the senior technical authority for how SIE builds, reuses and activates trusted data and intelligence.

This is one of SIE's most senior individual-contributor roles. You will report directly to the Vice President of the organization, sit on the leadership team alongside the Sr. Directors who run our two delivery portfolios, chair our architecture review forum, and lead a community of Principal Engineers anchored to the major architectural seams. You will have no direct reports by design; your leverage comes from the architecture, the standards and the decisions the whole organization builds against.

This is a hands-on role. You will author architectures and reference implementations, prototype uncertain approaches, review the designs and code that matter most, and step in directly when a technical problem puts an important outcome at risk.

What You'll Own

• A single, reusable understanding of the player. The context, lifecycle and lifetime-value intelligence that powers experiences and decisions across PlayStation, built once and reused rather than rebuilt per use case.


• Content and game understanding. Intelligence about titles, experiences and how players engage with them, connected through engagement and content graphs to improve discovery, personalization and new AI experiences.


• Creator and partner intelligence. Reusable title, launch and lifecycle intelligence available to partners at the point of decision, before launch, while live and after.


• Unified measurement. Experimentation, attribution and causal measurement so the company knows what changed an outcome, by how much, and what to do next.


• Activation. Real-time signals, activation data products and APIs that deliver trusted context into the systems that personalize and act.


• Semantic and conversational intelligence. A shared layer of business meaning and metrics that people, analytical tools and AI agents can query and trust.


• Enterprise decision data. Decision-grade finance and supply-chain data that accelerates transformation programs across SIE.


• Trusted foundations and golden paths. Data contracts, certification, lineage, unified events and standard publish-to-consume paths, with trust designed into the lifecycle; and launch data readiness so priority launches have their signals and measures from day one.

What You'll Do

Translate our multi-year Data & Intelligence strategy into a coherent technical strategy and target architecture, with clear principles for trusted data products, ML systems, shared intelligence, evaluation, activation and lifecycle management.

Chair the architecture review forum: set its charter and cadence, publish decisions, track adoption and hold the line on non-conforming designs.

Own the boundary decisions between platform capabilities built for reuse and domain delivery, and keep them stable.

Define and ratify the standards teams build against: data contracts, golden paths, model evaluation and experimentation, observability, reliability, cost and l