Forward Deployed Engineer
Crunchyroll · Los Angeles, California, United States
About this role
## Forward Deployed Engineer — Crunchyroll ### About the Role Crunchyroll is seeking a **Forward Deployed Engineer** to embed with teams across the company and build **AI solutions** that materially change how work gets done. You’ll work directly with business teams to understand real workflows, identify where AI/automation creates meaningful value, and take solutions from **prototype to production and adoption**. You’ll be part of **AI Enablement in the Executive Office**, turning high-value business problems into practical AI-powered systems—such as **agents, workflow automation, and applied AI**—and connecting models to the systems, tools, and data teams already use. You’ll also build the **context, evaluations, guardrails, and human checkpoints** needed for reliable real-world performance. ### In this role, you will: - **Embed deeply with business teams**: map real workflows, pain points, decisions, systems, and handoffs. - **Identify where AI creates business value**—starting from the problem, not the technology. - Choose the right technical approach (e.g., **agentic systems, generative AI, RAG, classical ML, deterministic automation, or simpler software**). - Translate ambiguous business needs into clear technical plans (architecture, data flows, integrations, permissions, success criteria, and build vs. buy decisions). - **Prototype quickly** to validate ideas before over-engineering. - Design and build **reliable AI systems end-to-end**, including tool use, orchestration, context/prompt engineering, state/memory, structured outputs, APIs, enterprise integrations, and human-in-the-loop workflows. - Engineer for the real world: anticipate **failure modes, tool errors, hallucinations, retries, permission boundaries, latency, cost, security constraints**, and graceful degradation. - Build **evaluations and observability** from the start (task success criteria, eval sets, tracing/logging/monitoring, cost/quality/drift). - Define expected **business outcomes** before building and measure impact after launch. - Work with business and technical leaders to align expectations, surface risks early, and keep stakeholders informed. - Stay close to users post-launch: observe performance in real workflows, iterate with feedback, and support adoption/change management. - Build for handoff and scale with clear ownership, documentation, monitoring, and maintenance paths. - Create reusable components, patterns, tools, and learnings to speed up future AI solutions. ### You have: - **6+ years** of software engineering experience, including recent experience shipping **applied AI/LLM systems in production**. - Experience designing/building/deploying AI systems that interact with **APIs, enterprise systems, data sources, or external tools** to complete multi-step tasks. - Strong **Python** skills and modern LLM application development experience (tool/function calling, orchestration, prompt/context engineering, structured outputs, **RAG**, model APIs, state management, enterprise integrations). - Ability to take AI solutions **end-to-end**: ambiguous problem → architecture → prototyping → implementation → deployment → iteration. - Experience engineering for **reliability** (failure handling, human-in-the-loop controls, guardrails, permissions, monitoring, cost and latency management). - Strong judgment on when to use an agent/RAG/ML/automation vs. conventional software. - Proven ability to work with **non-technical stakeholders** and translate bus
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