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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!
About the role
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In the role of Principal AI Engineer for Concepts Lab, you will report to the VP of the Concepts Lab.
We are considering applicants for the location of Los Angeles, CA (onsite).
As the Principal AI Engineer for Concepts Lab, you will be the technical anchor for everything AI in the lab: designing and building the agentic delivery tooling, evaluation harnesses, and paved paths that let cross\-functional pods take ideas from concept to production in days instead of months.
This is a hybrid technical and enablement role. You will move fluidly between hands\-on agent and platform development, instrumenting and measuring whether the system is actually working, and partnering with stakeholders across the company to roll those capabilities out. Success in your first year looks like a Concepts Lab where the cross\-functional pod model has been stress\-tested with real evidence, and where we can point to specific KPIs and say with confidence whether agentic delivery is making us faster and better, or whether we need to course\-correct.
### Core Areas of Responsibility
- Own the design and evolution of the lab’s agentic delivery system, including the agent harness, CLI and skill interfaces, workflow orchestration, isolation, evaluation, and observability layers that let agents safely read, write, refactor, and test code across our repos.
- Own the measurement layer that tells us whether agentic delivery is working. You will define and instrument the KPIs we will judge this program by, including cycle time (idea to prototype, decision to production), quality and rework rates, integration cost, eval pass rates, cost per cycle, pod throughput, adoption, and direction\-stability signals. You will build the dashboards and learning reports that turn the lab’s 2\-week sprints into evidence executives can act on, and you will be honest when the numbers say something is not working.
- Lead the integration work that makes agents effective in our environment, including context engineering, repo and tool access patterns, MCP servers, and the connective tissue between agents and the systems they need to reason about (codebases, docs, ownership, incidents, runtime).
- Drive enablement and adoption across Product, Design, and Engineering. You will run the rollouts that take the agentic stack from Concepts Lab into pilot teams and beyond: onboarding cross\-functional pods, codifying paved paths and best\-practice playbooks, embedding alongside teams during their first cycles, and using the feedback to harden the platform. You will be the person whose job it is to make the tooling actually get used, not just exist.
- Set the technical bar for the lab’s AI work, including design reviews, code quality, eval coverage, observability, cost management, and security practices, and mentor the engineers and contractors who collaborate with us.
- Be the opinionated voice on how cross\-functional pods should be designed and how agentic systems scale inside a large engineering organization. You will help leadership think clearly about pod composition, human touchpoints, where to keep humans in the loop and where to take them out, how to roll out team by team without breaking what works, and how to avoid the common failure modes (review overload, shadow product teams, prototypes that become unowned production systems). You will bring a strong point of view, defend it with evidence, and update it when the data says you were wrong.
About You
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We get excited about candidates, like you, because…
- You have 15\+ years of professional software engineering experience building production systems, with a track record of operating as a senior individual contributor or technical lead.
- You have hands\-on experience building and shipping agentic systems in production, including designing agent harnesses, tool\-use and MCP integrations, multi\-step workflows, and the eval and observability layers that keep them reliable.
- You possess deep expertise with modern agentic coding tools and frameworks (Claude Code, Cursor, Devin, or comparable) used across the full development cycle, and you can speak to where they break and how to extend them.
- You are equally strong in evaluation design, prompt and context engineering, and program\-level instrumentation, and have shipped agentic systems where you could point to numbers that demonstrated they were working (or not).
- You write production\-grade Python and/or TypeScript and have built developer\-facing tooling (CLIs, SDKs, internal platforms) that other engineers chose to adopt because it made their work easier, and you have rolled tools out across teams, not just shipped them.
- You hold strong, well\-defended opinions about how agentic systems scale across large engineering organizations, and you have either built that experience firsthand or studied closely how leading companies have done it.
- You have experience working in early\-stage, ambiguous environments where you define the problem as much as you solve it, and you know when to prototype quickly versus when to build for the long term.
Nice to Have
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- Experience in media, streaming, or gaming
- Experience working in an innovation lab, incubation environment, or R\&D organization
- Experience with Datadog, LangSmith, Langfuse, or comparable LLM observability tooling
About the Team
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The Concepts Lab is Crunchyroll’s R\&D and incubation group, focused on exploring new ideas, technologies, and experiences for anime fans. The team partners closely with engineering, product, and business leaders to test assumptions, build evidence, and transition validated concepts into delivery teams.
Our work spans early discovery through beta execution, with an emphasis on learning quickly, reducing risk, and setting teams up for long\-term success.
The Pay Range for this position is listed. Actual pay will vary based on factors including, but not limited to location, experience, and performance. The range listed is just one component of Crunchyroll’s Total Rewards offerings for employees. Other rewards may include performance bonuses, employer matched retirement savings, time\-off programs, and progressive health benefits and perks.
Pay Transparency \- Los Angeles, CA
$248,000 \- $300,000 USD
### 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.
Salary Context
This $248K-$300K range is above the 75th percentile 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 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($274K) sits 51% above the category median. Disclosed range: $248K to $300K.
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.
Crunchyroll AI Hiring
Crunchyroll has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US. Compensation range: $300K - $300K.
Location Context
AI roles in Los Angeles pay a median of $191,580 across 1,792 tracked positions. That's 4% below the national 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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