About Crunchyroll

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

We're looking for a Senior AI Solutions Engineer to embed with teams across Crunchyroll and build AI solutions that materially change how work gets done.

You'll go where the work is. You'll sit with teams, understand how their workflows actually operate, identify where AI or automation can create meaningful value, and rapidly build solutions alongside the people who will use them. This role combines strong business judgment and consultative problem-solving with hands-on engineering. You'll take problems from ambiguity through prototype, production, and adoption.

You'll be part of AI Enablement in the Executive Office, working across Crunchyroll to turn high-value business problems into practical AI-powered solutions, including agents, workflow automation, and other applied AI systems. You'll connect models to the systems, tools, and data teams already use, and build the context, evaluations, guardrails, and human checkpoints required for them to work reliably in the real world.

The goal is not to build AI for its own sake. You'll focus on problems where AI can materially improve speed, quality, cost, or the employee experience, and you'll measure whether what you build actually delivers that impact.

You'll work closely with Engineering, Enterprise Technology, Data & Insights, IT, Security, and other partners to ensure solutions are scalable, secure, and maintainable. Every engagement should also leave behind reusable components, patterns, and lessons that make the next solution faster to build.

In this role, you will

Embed deeply with business teams. Sit alongside them as work happens, map the real workflow rather than the documented one, and understand the pain points, decisions, systems, and handoffs that shape how work gets done.
Identify where AI can create meaningful business value. Start with the problem, not the technology, and be willing to conclude that AI is not the right answer.
Determine the right technical approach for each problem, whether that is an agentic system, generative AI, RAG, classical ML, deterministic automation, or a simpler software solution.
Translate ambiguous business problems into clear technical plans, including architecture, data flows, integration points, permissions, tool usage, success criteria, and build vs. buy vs. integrate decisions.
Prototype quickly to test whether an idea works before over-engineering it. Know when to build something in days to learn and when a problem warrants production-grade infrastructure.
Design and build reliable AI systems end-to-end, including tool use, orchestration, context and prompt engineering, state and memory, structured outputs, APIs, enterprise integrations, and human-in-the-loop workflows.
Engineer for the real world, not the demo. Anticipate failure modes, tool errors, hallucinations, retries, permission boundaries, latency, cost, security constraints, and graceful degradation.
Build evaluations and observability from the start. Define task-level success criteria and eval sets, and instrument tracing, logging, monitoring, cost, quality, and drift so performance is measurable in production.
Define the expected business outcome before you build and measure whether the solution actually improves the workflow after launch.
Work directly with business and technical leaders, translating between business needs and technical reality, setting clear expectations, surfacing risks early, and keeping stakeholders aligned as the solution evolves.
Stay close to users after launch. Observe how solutions perform in real workflows, iterate based on usage and feedback, and partner with the broader AI Enablement team on adoption and change management.