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About This Role
At Netflix, our mission is to entertain the world. Together, we are writing the next episode \- pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting\-edge technology. Come be a part of what’s next.
At Netflix, we want to entertain the world, and we're constantly innovating on how entertainment is imagined, created, and delivered to a global audience. Increasingly, that innovation is powered by AI — and Netflix is making a deliberate bet to become AI Native, with AI woven into how we build products, create content, and run the company.
The Opportunity
The Agent Platform team gives Netflix engineers the infrastructure to go from zero to a production\-grade AI agent without reinventing the wheel. We own the foundational building blocks the whole company builds on: the Model Gateway (unified access to external LLMs like Claude, GPT, and Gemini), the Assistance API for conversational use cases, the MCP Gateway that connects agents to Netflix's internal systems and knowledge, our Agent SDK (built on Strands and Claude), and an end\-to\-end evaluation stack via Braintrust.
The work has moved well beyond chat completions. Teams across Netflix are now building agents — systems that plan, call tools, observe results, and iterate — and they depend on us for the infrastructure to do that reliably and to know whether their agents are actually any good. We're a small team with outsized leverage: what we ship becomes the foundation for AI across all of Netflix.
What you will do:
- Design, build, and operate the Agent SDK and MCP Gateway that Netflix engineers use to build, deploy, and run AI agents in production.
- Build agents and agent infrastructure across the full lifecycle — plan/act/observe loops, tool and MCP integrations, deployment, and day\-2 operations.
- Make evaluation a first\-class part of the platform: build the tracing, eval suites, and quality signals that let teams measure agents, catch regressions, and iterate to make them better.
- Own reliability, observability, and guardrails for non\-deterministic systems running at very high scale
- Lead cross\-functional initiatives with ML scientists, data scientists, product managers, and other AI Platform teams.
- Rapidly iterate with users to improve the developer experience while establishing durable foundational capabilities.
Desired Background:
- 8\+ years of software engineering experience with a track record of delivering quality results.
- Hands\-on experience building, deploying, operating, AND evaluating LLM agents in production — not just chat\-completion apps or prototypes.
- Experience with one or more agent frameworks/SDKs (Strands, OpenAI Agents SDK, Anthropic Claude Agent SDK, LangGraph, pydantic\-ai, CrewAI, Google ADK) and with tool/function calling and MCP.
- Experience with LLM/agent evaluation and observability — building eval suites, tracing, and quality measurement, then iterating on results (Braintrust, LangSmith, W\&B, or equivalent).
- Strong experience building SDKs and APIs for internal or external developers.
- Strong fundamentals in building and operating scalable, observable, fault\-tolerant distributed systems.
- Proficiency in Python (and Python packaging tooling) plus one of Java, Go, C/C\+\+, Rust, or Zig. Familiarity with our stack — Temporal, FastAPI, PostgreSQL, Kubernetes — is a plus.
- Experience with large\-scale build, release, CI/CD, and observability methods.
Generally, our compensation structure consists solely of an annual salary; we do not have bonuses. You choose each year how much of your compensation you want in salary versus stock options. To determine your personal top of market compensation, we rely on market indicators and consider your specific job family, background, skills, and experience to determine your compensation in the market range. The range for this role is $466,000\.00 \- $750,000\.00\. This compensation range will vary based on location.
Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family\-forming benefits, and Life and Serious Injury Benefits. We also offer paid leave of absence programs. Full\-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full\-time salaried employees are immediately entitled to flexible time off. See more details about our Benefits here .
Netflix is a unique culture and environment. Learn more here .
Inclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner.
We are an equal\-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.
Job is open for no less than 7 days and will be removed when the position is filled.
Salary Context
This $466K-$750K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $190K across 219 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 3,823 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Netflix, 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 $232,000 based on 797 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($608K) sits 162% above the category median. Disclosed range: $466K to $750K.
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.
Netflix AI Hiring
Netflix has 8 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer, AI Product Manager, Data Scientist. Positions span Remote, US, New York, NY, US. Compensation range: $520K - $750K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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 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).
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 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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