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About This Role
Position Type:
Location(s):
United States, San Diego, CA
Date Posted:
Job ID:
R\-124264
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.
AI Software Engineer \- D2C \- SPOC
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Sony Interactive Entertainment (SIE) PlayStation; San Diego, CA
Who we are
The Direct to Consumer (D2C) Data Science organization comprises Data Science, Data Engineering and ML Engineering practices. D2C Data Science helps PlayStation grow and operate its digital business across commerce, subscriptions, payments, lifecycle experiences and player\-facing services. We partner with product, engineering, finance, marketing and operations teams to turn experimentation, forecasting, AI and production\-quality measurement into better player experiences.
Role overview
Within D2C Data Science, the D2C ML Engineering team is seeking an AI Software Engineer to help design, build, and support production AI capabilities that solve high\-value business problems across SPOC and the broader digital commerce ecosystem. This is not a model\-training or predictive\-platform ownership role; it is an applied AI engineering role focused on turning AI into dependable products, services, and automation.
You will work with core engineering, operations, data, risk, and product partners to build AI\-powered workflows that improve speed, quality, insight, and decision support. The work may include LLM\-powered services, retrieval\-augmented generation (RAG), agentic workflows, tool/function calling, evaluation harnesses, guardrails, and reusable AI platform components.
The ideal candidate is a strong software engineer with backend or platform experience and practical hands\-on applied AI experience. You are comfortable building with foundation model APIs, vector and hybrid search, prompt and model evaluation, AI observability, and cloud\-based services, and you are eager to learn while contributing to reliable production systems.
What you’ll be doing
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Build Applied AI Features: Implement services, workflows, and reusable components for LLM\-powered automation, retrieval, tool use, summarization, classification, decision support, and knowledge workflows.
Solve Business Problems with AI: Collaborate with operations, product, data, risk, and engineering stakeholders to understand use cases, prototype solutions, measure outcomes, and help move proven capabilities into production.
Support Agentic Workflows and Integrations: Build AI workflows that use tool/function calling, structured outputs, workflow state, internal APIs, and human review patterns to take useful action while staying auditable and controlled.
Develop Retrieval and Knowledge Systems: Contribute to RAG and agentic retrieval pipelines over enterprise content and operational data using embeddings, vector databases, hybrid search, reranking, citations, access controls, and freshness strategies.
Improve AI Quality, Safety, and Evaluation: Create and maintain evaluation suites, regression tests, prompt/model versioning, trace analysis, guardrails, policy checks, PII handling, hallucination mitigation, and operational monitoring.
Production AI Engineering: Develop scalable APIs, microservices, and event\-driven workflows in Python or Java, with attention to reliability, resilience, security, cost efficiency, and clean integration with existing services.
Cloud Delivery and Automation: Deploy AI services using AWS, containers, infrastructure as code, CI/CD pipelines, secrets management, observability, and operational runbooks.
Cross\-Functional Collaboration: Participate in design reviews, implementation planning, troubleshooting, documentation, and knowledge sharing across technical and non\-technical teams.
What we’re looking for
Educational Background: Bachelor's degree in computer science, engineering, a related technical field, or equivalent practical experience, with 2\+ years of professional software engineering experience.
Applied AI Experience: Hands\-on experience building AI or generative AI features that connect model APIs to business workflows, data, documents, or internal services.
Coding Proficiency: Strong software engineering skills in Python and/or Java, including API development, testing, debugging, asynchronous processing, and maintainable service design.
Cloud Competency: Experience with AWS or equivalent cloud services
RAG and Retrieval Systems: Familiarity with embeddings, chunking, indexing, retrieval strategies, vector and hybrid search, reranking, citations, and vector stores such as OpenSearch, Pinecone, Weaviate, Redis, pgvector, Azure AI Search, or similar technologies.
Agent and Workflow Orchestration: Experience with AI orchestration patterns and tools such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, OpenAI Agents SDK, N8N, AWS Bedrock Agents and Knowledge Bases, or comparable tools.
Structured Outputs and Tool Use: Experience designing prompts, schemas, tool/function calls, workflow contracts, and validation logic so AI systems can produce dependable outputs and interact safely with internal systems.
AI Observability and Evaluation: Familiarity with tracing, monitoring, evals, prompt testing, quality metrics, and debugging tools such as LangSmith, Arize Phoenix, OpenTelemetry, Datadog, Splunk, New Relic, CloudWatch, or comparable platforms.
Communication Skills: Possesses exceptional communication skills, able to turn business requirements into technical tasks, collaborate across teams, and explain AI tradeoffs in clear, practical terms.
Preferred Skills
Model Context and Connectors: Familiarity with Model Context Protocol (MCP) or similar patterns for connecting AI applications to enterprise tools, databases, documents, and workflows.
Multimodal AI Systems: Experience with text, image, document, audio, or video models, including multimodal embeddings, OCR/document understanding, or content moderation workflows.
Commerce or Trust Domain Experience: Experience applying AI to fraud, payments, risk, customer support, marketplace operations, trust and safety, content operations, or digital commerce business processes.
PlayStation isn't just the Best Place to Play \- it's also the Best Place to Work. We've thrilled gamers since 1994 when we launched the original PlayStation. Today, we're recognized as a global leader in interactive and digital entertainment. The PlayStation brand falls under Sony Interactive Entertainment, a wholly owned subsidiary of Sony Corporation.
Sony is an Equal Opportunity Employer. All persons will receive consideration for employment without regard to race, color, religion, gender, pregnancy, national origin, ancestry, citizenship, age, legally protected physical or mental disability, covered veteran status, status in the U.S. uniformed services, sexual orientation, marital status, genetic information 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.
We sincerely appreciate the time and effort you spent in contacting us and we thank you for your interest in PlayStation.
At SIE, we consider several factors when setting each role’s base pay range, including the competitive benchmarking data for the market and geographic location.
Please note that the base pay range may vary in line with our hybrid working policy and individual base pay will be determined based on job\-related factors which may include knowledge, skills, experience, and location.
In addition, this role is eligible for SIE’s top\-tier benefits package that includes medical, dental, vision, matching 401(k), paid time off, wellness program and coveted employee discounts for Sony products. This role also may be eligible for a bonus package.
The estimated base pay range for this role is listed below.
$150,000—$225,000 USD
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 $150K-$225K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At PlayStation, 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 Required
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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. Disclosed range: $150K to $225K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
PlayStation AI Hiring
PlayStation has 2 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: $225K - $274K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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