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About This Role
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.
Software Development Engineer In Test, ML/AI
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PlayStation offers more than just the Best Place to Play; it is also a top workplace. Today, we are a global entertainment leader. Our portfolio includes PlayStation®5, PlayStation®4, PlayStation®VR, PlayStation®Plus, and well\-known software from PlayStation Studios.
PlayStation works to foster an inclusive environment where employees feel supported and diversity is valued. We encourage individuals with passion and curiosity for innovation, technology, and play to apply for our open roles and become part of our growing distributed team.
The PlayStation brand falls under Sony Interactive Entertainment, a wholly\-owned subsidiary of Sony Group Corporation.
At SIE, we believe in making play safer for everyone. Within our Information Security team, we protect our people, platforms, and products with smart, scalable, and thoughtful solutions. Our mission is to provide clear, actionable risk and security insights that guide the business and enable amazing player experiences.
We're building a team that thrives on collaboration, curiosity, and delivering real value. Come help us make a difference!
Overview
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We are hiring a Software Engineer in Test specializing in ML/AI quality, including automation for model evaluation, LLM\-assisted test generation, and validation of AI\-powered workflows. The role includes guiding quality strategy and developing automation frameworks. You will also manage implementation for complex, cross\-functional, machine learning\-powered products and services. This role involves more than test development. You will lead quality initiatives from start to finish, ensuring teams stay synchronized, dependencies are managed, and releases are delivered confidently. You will work closely with ML, engineering, product, and infrastructure teams. You will shape how quality is built, monitored, and expanded while leading embedded QE efforts within projects.
What you'll do
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- Define and complete quality strategies, test plans, and automation coverage for ML\-powered services and platform components.
- Use LLMs and other AI\-assisted techniques to generate, expand, and maintain high\-value test cases for ML\-powered workflows.
- Design scenario\-based test suites for AI features, including adversarial prompts, edge cases, ambiguous inputs, and underrepresented user scenarios.
- Lead QE efforts for multi\-functional projects, driving risk assessment, dependency management, and release readiness.
- Design, develop, and maintain scalable automation frameworks for backend services, APIs, and ML inference systems using Python and/or Java.
- Build automated validation for ML and LLM outputs, including ranking behavior, score distributions, prompt/response quality, hallucination indicators, and probabilistic model evaluation.
- Debug test failures, service anomalies, model inconsistencies, and AI behavior regressions to identify root causes and drive resolution.
- Perform functional, integration, regression, API, end\-to\-end, performance, and reliability testing for distributed systems.
- Improve automation reliability, reduce flakiness, and optimize execution efficiency.
- Partner with engineering and ML teams to integrate automated testing into CI/CD pipelines and release workflows.
- Collaborate across teams to establish scalable quality standards, tooling, and guidelines.
Required qualifications
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- Bachelor's degree in Computer Science or equivalent practical experience.
- 3\+ years of experience as an SDET or QE engineer focused on backend and distributed systems.
- Experience using LLMs to generate, transform, and prioritize test cases for AI\-powered experiences.
- Experience with AI evaluation tooling, prompt evaluation frameworks, model monitoring, or human\-in\-the\-loop review workflows.
- Strong experience testing RESTful APIs, microservices, and distributed architectures.
- Proficiency in Python, Java, JS or similar languages for automation development.
- Hands\-on experience with automation frameworks such as pytest, JUnit, Selenium, Playwright, Cypress, or Appium.
- Experience with CI/CD systems and test pipelines (Jenkins, GitHub Actions, etc.).
- Experience with cloud and container technologies (AWS, GCP, Kubernetes, Docker).
- Familiarity with databases, monitoring, and observability tools.
- Strong understanding of SDLC, Agile methodologies, and release processes.
- Excellent problem\-solving, debugging, and communication skills.
Preferred qualifications
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- Experience validating ML outputs using statistical analysis or scenario\-based testing approaches.
- Familiarity with ML infrastructure, data pipelines, or model\-serving platforms (Seldon, KServe, Ray Serve, etc.).
- Prior work in content moderation ML, security, fraud detection, or adversarial ML.
- Experience testing high\-scale, low\-latency online services.
- Experience with Databricks or similar ML platform tooling.
- Familiarity with Node.js, React, or modern frontend technologies.
- Experience testing mobile, console, or other non\-PC platforms.
What sets you apart
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- Strong combination of automation engineering and delivery ownership.
- Ability to drive quality across complex cross\-functional initiatives.
- Practical understanding of how to test non\-deterministic AI systems and separate model variance from quality regressions.
- Proven risk management and dependency coordination skills.
- Ability to influence engineering teams and promote quality guidelines.
- Passion for scalable, reliable, and maintainable automation systems.
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 $140K-$210K range is below the median for AI Product Manager roles in our dataset (median: $187K across 164 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 4,133 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At PlayStation, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $213,800 based on 610 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($175K) sits 18% below the category median. Disclosed range: $140K to $210K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
PlayStation AI Hiring
PlayStation has 3 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Positions span San Francisco, CA, US, San Diego, CA, US. Compensation range: $210K - $274K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,258 tracked positions. That's 26% above the national median.
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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