Senior Principal Engineer, Data & Machine Learning

PlayStation Global•San Mateo, CA

4d•$304,200 - $456,200•Hybrid

About The Position

Sony Interactive Entertainment is seeking a Senior Principal Engineer, Data & Machine Learning to serve as chief architect for its Data & Intelligence organization and senior technical authority for how the company builds, reuses, and activates trusted data and intelligence. The role shapes architecture across PlayStation’s data and machine-learning initiatives, serving an ecosystem with more than 100 million people, tens of petabytes of data, and trillions of telemetry events annually. This is one of SIE’s most senior individual-contributor positions. The engineer reports directly to the Vice President of the organization, sits on its leadership team, chairs the architecture review forum, and leads a community of Principal Engineers. The role has no direct reports and is hands-on, including authoring architectures and reference implementations, prototyping approaches, reviewing important designs and code, and directly addressing critical technical problems. The role sets technical direction across reusable player, content, creator and partner intelligence; unified measurement and activation; semantic and conversational intelligence; enterprise decision data; and trusted data foundations. It translates strategy into target architecture and standards, guides complex cross-organizational initiatives, embeds security, privacy, governance and responsible-AI controls, reduces duplication, and connects technical investment to measurable outcomes. The position is based in San Mateo, California, and the posting notes a hybrid working policy. The estimated base pay range is $304,200–$456,200 USD. The role is eligible for SIE’s top-tier benefits package and may also be eligible for a bonus package. SIE conducts background checks at the offer stage for new employees.

Requirements

15+ years in software, data, or machine-learning engineering, including 9+ years leading the architecture and delivery of large-scale, complex systems.
Track record of authoring architectures, standards, or platforms adopted across multiple organizations and sustained in use.
Experience taking data, ML, or decisioning systems serving tens of millions of consumers into production and operating them at scale.
Experience chairing, or serving as the deciding technical voice in, an architecture review forum spanning multiple engineering organizations.
Deep expertise in at least two of the following: reusable data products, semantic and metadata layers and data contracts; ML platforms, lifecycle management, evaluation and decisioning; real-time signals, distributed inference and activation platforms; experimentation and causal measurement; generative AI, retrieval and agentic systems; responsible AI, privacy and technical governance.
Strong command of distributed systems, cloud, data architecture, APIs, security, reliability, observability, and performance engineering.
Demonstrated ability to influence senior executives, engineering and product leaders, and cross-functional partners without direct authority.
Outstanding written communication; the role leads through documents.
Experience mentoring Staff, Principal, or similarly senior technical professionals.
Master’s degree in Computer Science, Machine Learning, Engineering, or a related discipline, or equivalent experience.

Nice To Haves

Experience applying data and ML in gaming, entertainment, media, digital commerce, or another large-scale consumer ecosystem.
Experience with personalization, recommendations, customer intelligence, content intelligence, experimentation, or predictive decisioning at scale.
Experience with shared data or ML platforms serving multiple products and organizational domains.
Experience with feature, vector, graph, semantic, or knowledge platforms; AI gateways and LLMOps.
Experience translating research or emerging technology into production capabilities.
Contributions to the field through open source, patents, publications, standards, or conference talks.

Responsibilities

Translate the multi-year Data & Intelligence strategy into a coherent technical strategy and target architecture, including principles for trusted data products, ML systems, shared intelligence, evaluation, activation, and lifecycle management.
Chair the architecture review forum by setting its charter and cadence, publishing decisions, tracking adoption, and enforcing architectural standards.
Own and maintain boundary decisions between reusable platform capabilities and domain delivery.
Define and ratify standards for data contracts, golden paths, model evaluation and experimentation, observability, reliability, cost, and lifecycle management.
Lead the technical direction of complex cross-organizational initiatives and author proposals, reference designs, and prototypes that establish a path from concept to production.
Work with engineering and data science teams to remove critical blockers and turn architectural decisions into usable capabilities.
Partner with security, privacy, legal, governance, and responsible-AI teams to embed controls throughout the data and ML lifecycle, including the use of proprietary data, third-party and foundation models, and externally hosted AI services.
Identify duplicative capabilities, drive rationalization and retirement, and improve interoperability across SIE’s data and ML landscape.
Advise senior executives on major technical investments, tradeoffs, emerging capabilities, and long-term risk; produce written recommendations for annual planning.
Connect technical investment to named consumers, decisions, and measurable outcomes; define measures for adoption, reuse, time-to-action, quality, reliability, safety, and cost.
Lead a community of Principal Engineers and mentor Principal, Staff, and Senior engineers, architects, ML engineers, and data scientists.
Represent SIE’s data and machine-learning direction in senior leadership forums, partner organizations, and, where appropriate, the broader industry.
Set architecture and drive reuse across player understanding, content and game understanding, creator and partner intelligence, unified measurement, activation, semantic and conversational intelligence, enterprise decision data, and trusted foundations.