Interested in this AI/ML Engineer role at Electronic Arts?
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Description \& Requirements
Electronic Arts creates next\-level entertainment experiences that inspire players and fans around the world. Here, everyone is part of the story. Part of a community that connects across the globe. A place where creativity thrives, new perspectives are invited, and ideas matter. A team where everyone makes play happen.
Founding Engineer
The future of entertainment is interactive, and our Ventures Group plays an important role in this future by building content, culture, and community that take us Beyond Gaming.
Beyond our existing franchises, Ventures is focused on incubating bold new ideas \- exploring emerging technologies, new platforms, and entirely new forms of interactive entertainment.
What We’re Building
We’re launching a new wave of incubation within EA \- small, highly autonomous teams exploring 0 1 product opportunities.
As a Founding Engineer , you'll partner directly with a Product Founder to rapidly prototype, launch, and scale new ventures. This is not a traditional engineering role focused on a single component or service. You'll be responsible for architecting and building entire products \- from frontend (web and native mobile) experiences to backend systems, infrastructure, AI integrations, and operational tooling.
You’ll leverage modern AI\-native development workflows to dramatically increase velocity while maintaining a high bar for quality, scalability, and user experience.
What You’ll Do
Architect and Build New Products
- Design and develop end\-to\-end product experiences across web, mobile, backend services, and cloud infrastructure.
- Establish scalable technical foundations that enable rapid experimentation without sacrificing long\-term maintainability.
- Make pragmatic architectural decisions that balance speed, quality, and future growth.
Lead AI\-Native Development
- Operate multiple AI coding agents and development tools to accelerate product development.
- Develop workflows that combine human judgment with AI\-assisted implementation, testing, debugging, and iteration.
- Continuously evaluate emerging AI tooling and integrate it into the product development process.
Own Technical Execution
- Translate product concepts into working software with exceptional speed and quality.
- Build APIs, services, data pipelines, frontend (web and native mobile) experiences, and supporting infrastructure as needed.
- Maintain a strong focus on performance, reliability, security, and scalability.
Scale Successful Ventures
- Evolve prototypes into production\-ready systems capable of supporting growth.
- Implement observability, monitoring, deployment pipelines, and operational best practices.
- Continuously improve architecture as products mature and user adoption increases.
Contribute to the Ventures Playbook
- Share technical learnings, AI workflows, architectural patterns, and development practices across EA Ventures.
- Help establish new ways of building products that can be replicated across future incubation efforts.
What You Bring
- 10\+ years of experience building and shipping full\-stack products from concept to launch.
- Strong software engineering fundamentals across frontend (web and native mobile), backend, cloud infrastructure, and distributed systems.
- Proven ability to operate independently and make high\-quality technical decisions with limited guidance.
- Experience designing scalable architectures and modern cloud\-native systems.
- Familiarity with AI\-assisted development tools and excitement about AI\-native engineering workflows.
- Ability to move quickly while maintaining strong engineering judgment.
- Excellent problem\-solving and communication skills.
- Passion for building products from 0 1 and a strong understanding how to scale to millions of users
Bonus Points
- Startup or founding engineer experience.
- Experience building consumer\-facing applications in gaming, sports, social, media, or entertainment.
- Experience building AI\-powered products, agents, orchestration systems, or LLM\-based applications.
- Familiarity with modern mobile development ecosystems.
- Experience with cloud platforms, CI/CD, observability, and production operations.
- Experience scaling products from prototype through growth stages up to millions of users.
Why Join Us
- Build entirely new products from scratch with the backing of EA's resources, reach, and expertise.
- Work in a small, highly autonomous team where your decisions directly shape the product and business.
- Leverage cutting\-edge AI tooling and help define the future of software development within EA.
- Contribute to a portfolio of new ventures that could become the next generation of interactive entertainment experiences.
5\-Bullet Summary
- Partner with a Product Founder to build and launch new 0 1 ventures , owning the technical architecture and implementation from day one.
- Design and develop full\-stack products , spanning frontend (web and native mobile), backend, infrastructure, AI integrations, and operational systems.
- Leverage multiple AI coding agents and AI\-native workflows to dramatically accelerate product development and experimentation.
- Create scalable, production\-ready architectures that can evolve from prototype to successful consumer product.
- Share technical learnings and best practices to help establish EA Ventures' broader incubation and engineering playbook.
Pay Transparency \- North America
COMPENSATION AND BENEFITS
The ranges listed below are what EA in good faith expects to pay applicants for this role in these locations at the time of this posting. If you reside in a different location, a recruiter will advise on the applicable range and benefits. Pay offered will be determined based on a number of relevant business and candidate factors (e.g. education, qualifications, certifications, experience, skills, geographic location, or business needs).
PAY RANGES
\* California (depending on location e.g. Los Angeles vs. San Francisco) \*$193,100 \- $296,500 USD
Pay is just one part of the overall compensation at EA.
In the US, we offer a package of benefits including paid time off (3 weeks per year to start), 80 hours per year of sick time, 16 paid company holidays per year, 10 weeks paid time off to bond with baby, medical/dental/vision insurance, life insurance, disability insurance, and 401(k) to regular full\-time employees. Certain roles may also be eligible for bonus and equity.
*About Electronic Arts*
We’re proud to have an extensive portfolio of games and experiences, locations around the world, and opportunities across EA. We value adaptability, resilience, creativity, and curiosity. From leadership that brings out your potential, to creating space for learning and experimenting, we empower you to do great work and pursue opportunities for growth.
We adopt a holistic approach to our benefits programs, emphasizing physical, emotional, financial, career, and community wellness to support a balanced life. Our packages are tailored to meet local needs and may include healthcare coverage, mental well\-being support, retirement savings, paid time off, family leaves, complimentary games, and more. We nurture environments where our teams can always bring their best to what they do.
Electronic Arts is an equal opportunity employer. All employment decisions are made without regard to race, color, national origin, ancestry, sex, gender, gender identity or expression, sexual orientation, age, genetic information, religion, disability, medical condition, pregnancy, marital status, family status, veteran status, or any other characteristic protected by law. We will also consider employment qualified applicants with criminal records in accordance with applicable law. EA also makes workplace accommodations for qualified individuals with disabilities as required by applicable law.
Salary Context
This $193K-$296K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Electronic Arts, 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 $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($244K) sits 32% above the category median. Disclosed range: $193K to $296K.
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.
Electronic Arts AI Hiring
Electronic Arts has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Redwood City, CA, US. Compensation range: $296K - $296K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 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).
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 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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