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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
You will set the architecture for, and drive reuse across, the initiatives that define the next three years of data and intelligence at PlayStation:
- 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 lifecycle management.
- Lead the technical direction of our hardest cross-organizational initiatives; author proposals, reference designs and prototypes that establish a credible path from concept to production.
- Work directly with engineering and data science teams to remove critical blockers, so that decisions become usable capabilities rather than architecture on paper.
- Partner with security, privacy, legal, governance and responsible-AI teams to embed controls throughout the data and ML lifecycle, including the responsible 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 the written recommendations that drive annual planning.
- Connect technical investment to named consumers, decisions and measurable outcomes; define how we measure adoption, reuse, time-to-action, quality, reliability, safety and cost.
- Lead a community of Principal Engineers; 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.
What We're Looking For
- 15+ years in software, data or machine learning engineering, including 9+ years leading the architecture and delivery of large-scale, complex systems.
- A track record of authoring architectures, standards or platforms that were adopted across multiple organizations and stayed 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 being the deciding technical voice in, an architecture review forum spanning multiple engineering organizations.
- Deep expertise in at least two of: 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. This 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 Have
- Data and ML applied in gaming, entertainment, media, digital commerce or another large-scale consumer ecosystem.
- Personalization, recommendations, customer intelligence, content intelligence, experimentation or predictive decisioning at scale.
- Shared data or ML platforms serving multiple products and organizational domains.
- 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.
Why You'll Love This Role
- Set the technical direction for data and intelligence across PlayStation.
- Own the architecture behind experiences reaching more than 100 million people.
- Report directly to the VP and sit on the organization's leadership team.
- Build once, reuse everywhere: the mandate is to end duplicated work, not add to it.
- Work with strong Principal and Staff engineers, data scientists and product leaders across the company.
- Stay hands-on at the most senior IC level.
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Please note, Sony Interactive Entertainment conducts background checks at the offer stage for all new employees (which may include criminal background checks for some roles) and will need to process personal information to support these checks.
Please refer to our Candidate Privacy Notice for more information about what personal information we collect, how we use it, who we share it with, and your data protection rights.
Equal Opportunity Statement:
Sony is an Equal Opportunity Employer. All persons will receive consideration for employment without regard to gender (including gender identity, gender expression and gender reassignment), race (including colour, nationality, ethnic or national origin), religion or belief, marital or civil partnership status, disability, age, sexual orientation, pregnancy, maternity or parental status, trade union membership or membership in any other legally protected category.
We strive to create an inclusive environment, empower employees and embrace diversity. We encourage everyone to respond.
Sony Interactive Entertainment is a Fair Chance employer and qualified applicants with arrest and conviction records will be considered for employment.
Salary Context
This $304K-$456K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Sony Interactive Entertainment, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($380K) sits 79% above the category median. Disclosed range: $304K to $456K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Sony Interactive Entertainment AI Hiring
Sony Interactive Entertainment has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Safety. Positions span San Mateo, CA, US, San Diego, CA, US, Austin, TX, US. Compensation range: $199K - $456K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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