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About This Role
Company Description
DreamWorks Animation is looking for more adventurous dreamers who shoot for the moon. We tell stories about the journeys our unconventional heroes take to make dreams come true. As a growth-minded studio, we pride ourselves on being one of the world's leading producers of high-quality, award-winning, animated films and series, reaching consumers around the globe. We push to feel more, laugh more, and build immersive new worlds.
DreamWorks creates a diverse array of original content in a variety of formats, delivering compelling stories with unique characters. We place tremendous value on the experiences our talent brings to the table from their own non-traditional paths to success. We believe in frequent communication and that transparency and trust yield the best work. We are a community of artists, technologists, innovators, and creators, who are passionate about animation and also happen to love eating lunch together.
DreamWorks Animation’s Studio Platform Engineering team builds the systems and infrastructure that enable our artists and technology teams to operate at scale. We own the Feature Animation platform, streamline software delivery through modern CI/CD practices, and lead the Studio’s observability ecosystem. You’ll play a key role in designing and evolving the next generation of resilient, cloud-native, AI-enabled platforms that drive innovation across the Studio.
We are seeking a Senior AI Software Engineer with a strong background in DevOps, Kubernetes infrastructure, and Python development to build, scale, and secure our AI infrastructure used for data workflows and management. In this role, you will bridge the gap between traditional infrastructure operations and advanced AI engineering. Instead of just consuming AI models, you will engineer the enterprise-grade ecosystem that hosts, monitors, scales, and protects our next generation of resilient, cloud-native, AI-enabled platforms that drive innovation across the Studio in an 'Artists First' methodology.
If you are part of the fandom and believe teamwork makes the dream work, join us in #livingthedream and #doingyourdreamwork!
Job Description
“What would you say you do here?”
- Collaborate across technology and production teams including principal engineers to shape the best practices of Platform as a Service implementation hosting stateless applications and AI infrastructure
- Influence and contribute to the Agentic Engineering and MCP Server Development efforts
- Major contributor to the development and adoption of governance, security, and observability best practices for AI and agentic systems, helping establish standards for secure, reliable, responsible, and observable AI agent development and operations
- Leverage Kubernetes orchestration and operators to automate stateless application deployments and administration across multiple environments (on-premises and in the cloud)
- Leverage Enterprise grade Kubernetes software stack solution for developing, deploying, and running AI workloads on a Kubernetes platform across development and production environments
- Identify areas for process and efficiency improvement within Platform Services; recommend solutions and assist in overseeing implementation. Actively facilitate continuous improvement.
- Monitor and maintain platform infrastructure, utilizing tools like Datadog for performance tracking, alerts, and capacity management
- Participate in continuous organizational cyber security vulnerability mitigation and compliance efforts
- Define and document standard run books and operating procedures. Create and maintain system information and architecture diagrams
- Develop and maintain microservices, tools and automation
- Provide software troubleshooting and break-fix support for production
Qualifications
“What do I need to have in order to do this job?”
- Bachelor’s degree in Computer Science, Engineering, or a related technical field
- 8+ years of experience in Software, DevOps or Platform Engineering, with 1+ years building AI systems for real workflows.
- Hands-on experience in cloud-native technologies and architectures (Docker, Kubernetes).
- Proven experience developing and deploying AI agents, tool-calling systems, and multi-agent systems
- Proficiency in revision control and DevOps best practices (Git)
- Expert Linux experience (Red Hat, CentOS) and proficient with one or more Unix shell scripting languages (Bash, C-Shell)
- Proficient in either Python, Java, or JavaScript development
- Operational support experience, including platform infrastructure monitoring and troubleshooting using tools like Datadog, Prometheus, or similar
“What can I offer?”
- Strong, hands-on experience managing and troubleshooting production workloads running on Kubernetes
- Hands-on experience developing or deploying custom application servers, Retrieval-Augmented Generation (RAG) models and Model Context Protocols (MCP) servers
- Deep understanding of LLM model integration and tuning
- Experience monitoring and mitigating the specific operational costs associated with token usage and GPU compute utilization
- Deep expertise in at least one major cloud ecosystem (AWS, Azure, OCI or GCP) and infrastructure provisioning
- Experience with designing automation that reduces manual work in complex, multi-stakeholder environments
- Experience with AMD Inference Microservice Platform (AIMS) or NVIDIA NIM a plus
- Excellent communication skills and the ability to translate fluently between artists, production management, and engineers
- Genuine passion for animation, storytelling, or creative productions
This position has been designated as fully remote, meaning that the position is expected to contribute from a non-DreamWorks worksite, most commonly an employee’s residence.
This position is eligible for company sponsored benefits, including medical, dental and vision insurance, 401(k), paid leave, tuition reimbursement, and a variety of other discounts and perks. Learn more about the benefits offered by NBCUniversal by visiting the Benefits page of the Careers website.
Salary range: $155,000 - $190,000
Additional Information
As part of our selection process, external candidates may be required to attend an in-person interview with an NBCUniversal employee at one of our locations prior to a hiring decision. NBCUniversal's policy is to provide equal employment opportunities to all applicants and employees without regard to race, color, religion, creed, gender, gender identity or expression, age, national origin or ancestry, citizenship, disability, sexual orientation, marital status, pregnancy, veteran status, membership in the uniformed services, genetic information, or any other basis protected by applicable law.
If you are a qualified individual with a disability or a disabled veteran, you have the right to request a reasonable accommodation if you are unable or limited in your ability to use or access nbcunicareers.com as a result of your disability. You can request reasonable accommodations by emailing [email protected].
For LA County and City Residents Only: NBCUniversal will consider for employment qualified applicants with criminal histories, or arrest or conviction records, in a manner consistent with relevant legal requirements, including the City of Los Angeles' Fair Chance Initiative For Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, where applicable.
Salary Context
This $155K-$190K range is below the median for AI Software Engineer roles in our dataset (median: $193K across 172 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 9,653 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At NBCUniversal, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $220,000 based on 1,448 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($172K) sits 22% below the category median. Disclosed range: $155K to $190K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
NBCUniversal AI Hiring
NBCUniversal has 2 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span Glendale, CA, US, New York, NY. Compensation range: $190K - $190K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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