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About This Role
About Crunchyroll
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Founded by fans, Crunchyroll delivers the art and culture of anime to a passionate community. We super\-serve over 100 million anime and manga fans across 200\+ countries and territories, and help them connect with the stories and characters they crave. Whether that experience is online or in\-person, streaming video, theatrical, games, merchandise, events and more, it’s powered by the anime content we all love.
Join our team, and help us shape the future of anime!
Enterprise Technology is building the core AI\-native engineering capabilities needed to improve how we design, build, test, and ship software. This role will help apply AI\-assisted engineering across Enterprise Technology projects while maintaining strong quality, security, and production\-readiness standards.
You will build production software, direct AI coding agents, review AI\-generated work, improve delivery practices, and help turn what works into reusable patterns for future Enterprise Technology initiatives.
In the role of Staff AI Engineer, you will report to the VP, Enterprise Technology. We are considering applicants for the location of Dallas, Texas (on\-site Tuesdays, Wednesdays, and Thursdays).
What You’ll Do:
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- Build and ship production software for Enterprise Technology initiatives, including services, APIs, data models, user interfaces, integrations, tests, and operational tooling.
- Direct AI coding agents by writing clear specs, acceptance criteria, implementation plans, and verification steps.
- Review and own AI\-generated code, tests, pull requests, and migrations for correctness, security, maintainability, and production readiness.
- Help establish repeatable AI\-assisted engineering practices for Enterprise Technology projects.
- Design and implement integration\-heavy, data\-intensive, and workflow\-driven systems for enterprise applications and operational platforms.
- Apply and extend AI\-assisted engineering tooling, including coding\-agent workflows, repo access patterns, context setup, automated verification, and reusable delivery templates.
- Track practical delivery metrics such as cycle time, quality, rework, test coverage, cost, adoption, and operational health.
- Partner with Product, Security, business stakeholders, and Enterprise Technology teams to deliver secure, maintainable software.
- Improve testing, review, observability, documentation, and operational practices for AI\-assisted delivery.
About You:
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We get excited about candidates, like you, because...
- You have 8\+ years of experience building and shipping production software as a hands\-on engineer, senior IC, technical lead, or Staff\-level contributor.
- You have strong production experience with Python and/or TypeScript across backend, frontend, data, API, or cloud\-native systems.
- You have hands\-on experience using AI coding agents or tools such as Claude Code, Google Antigravity, Codex, Cursor, or similar systems.
- You have strong engineering judgment for reviewing AI\-generated code and knowing where human oversight is required.
- You have experience building or integrating services, APIs, relational data stores, event\-driven systems, and user\-facing workflows.
- You're familiar with cloud\-native development, preferably GCP, including CI/CD, monitoring, security scanning, and cost\-aware engineering.
- You understand secure engineering practices, role\-based access, SSO, audit logging, and controls for enterprise systems.
- You're able to influence technical decisions, mentor engineers, and raise quality across a significant system or project area.
- You are able to communicate with both engineering and non\-engineering stakeholders.
A plus if you have:
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- Experience with enterprise systems, business applications, workflow platforms, data platforms, or internal tools.
- Experience with media, streaming, content platforms, or complex business domains.
- Experience building developer\-facing tools, templates, or internal platforms that help engineers ship software faster and more safely.
- Experience with LLM or agent observability tools such as Datadog, LangSmith, Langfuse, or similar systems.
- Experience with data migration, compliance\-sensitive systems, SOX controls, audit\-heavy environments, or complex enterprise integrations.
Why you will love working at Crunchyroll
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In addition to getting to work with fun, passionate and inspired colleagues, you will also enjoy the following benefits and perks:
- Receive a great compensation package including salary plus performance bonus earning potential, paid annually.
- Flexible time off policies allowing you to take the time you need to be your whole self.
- Generous medical, dental, vision, STD, LTD, and life insurance
- Health Saving Account HSA program
- Health care and dependent care FSA
- 401(k) plan, with employer match
- Employer paid commuter benefit
- Support program for new parents
- Pet insurance and some of our offices are pet friendly!
\#LifeAtCrunchyroll \#LI\-Hybrid
### About our Values
We want to be everything for someone rather than something for everyone and we do this by living and modeling our values in all that we do. We value
- Courage. We believe that when we overcome fear, we enable our best selves.
- Curiosity. We are curious, which is the gateway to empathy, inclusion, and understanding.
- Kaizen. We have a growth mindset committed to constant forward progress.
- Service. We serve our community with humility, enabling joy and belonging for others.
### Our commitment to diversity and inclusion
Our mission of helping people belong reflects our commitment to diversity \& inclusion. It's just the way we do business.
We are an equal opportunity employer and value diversity at Crunchyroll. Pursuant to applicable law, we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
Crunchyroll, LLC is an independently operated joint venture between US\-based Sony Pictures Entertainment, and Japan's Aniplex, a subsidiary of Sony Music Entertainment (Japan) Inc., both subsidiaries of Tokyo\-based Sony Group Corporation.
*Questions about Crunchyroll’s hiring process? Please check out our Hiring FAQs:* *https://help.crunchyroll.com/hc/en\-us/articles/360040471712\-Crunchyroll\-Hiring\-FAQs*
*Please refer to our Candidate Privacy Policy for more information about how we process your personal information, and your data protection rights:* *https://tbcdn.talentbrew.com/company/22978/v1\_0/docs/spe\-jobs\-privacy\-policy\-update\-for\-crpa\-dec\-21\-22\.pdf*
Please beware of recent scams to online job seekers. Those applying to our job openings will only be contacted directly from @crunchyroll.com email account.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Crunchyroll, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Crunchyroll AI Hiring
Crunchyroll has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Dallas, TX, US, Los Angeles, CA, US. Compensation range: $229K - $229K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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