Forward Deployed Engineer
Los Angeles · USD 183,400-229,200 · 6+ years experience · Hybrid · Posted Oct 1, 2026
At a glance
- Experience
- 6+ years experience
- Workplace
- Hybrid
- Location
- Los Angeles
- Salary
- USD 183,400-229,200
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
- Crunchyroll is looking for a Forward Deployed Engineer to embed with teams across the company and build AI solutions that materially change how work gets done.
- This role combines strong business judgment and consultative problem-solving with hands-on engineering, taking problems from ambiguity through prototype, production, and adoption.
- Part of AI Enablement in the Executive Office, the role turns high-value business problems into practical AI-powered solutions, including agents and workflow automation.
Responsibilities
- Embed deeply with business teams, sitting alongside them as work happens to map real workflows and understand pain points, decisions, systems, and handoffs.
- Identify where AI can create meaningful business value, starting with the problem and maintaining the willingness to conclude AI is not the right answer.
- Determine the right technical approach for each problem, whether agentic systems, generative AI, RAG, classical ML, deterministic automation, or simpler software.
- Translate ambiguous business problems into clear technical plans encompassing architecture, data flows, integration points, permissions, tool usage, success criteria, and build vs. buy decisions.
- Prototype quickly to test ideas before over-engineering, knowing when to build in days versus production-grade infrastructure.
- Design and build reliable AI systems end-to-end, including tool use, orchestration, prompt engineering, state and memory, structured outputs, APIs, enterprise integrations, and human-in-the-loop workflows.
- Engineer for the real world by anticipating failure modes, tool errors, hallucinations, retries, permission boundaries, latency, cost, security constraints, and graceful degradation.
- Build evaluations and observability from the start by defining task-level success criteria, eval sets, and instrumenting tracing, logging, monitoring, cost, quality, and drift.
- Define expected business outcomes before building and measure whether the solution actually improves the workflow after launch.
- Work directly with business and technical leaders, translating needs, setting expectations, surfacing risks, and building for handoff, documentation, and scale.
Requirements
- 6+ years of software engineering experience, including significant recent experience building and shipping applied AI or LLM-powered systems in production.
- Demonstrated experience designing, building, and deploying AI systems that interact with APIs, enterprise systems, data sources, or external tools to complete multi-step tasks.
- Strong Python skills and deep experience with modern LLM application development, including tool and function calling, orchestration, prompt and context engineering, structured outputs, RAG, model APIs, state management, and enterprise integrations.
- Experience taking AI solutions end-to-end, from ambiguous problem statements through architecture, prototyping, implementation, deployment, and iteration.
- Experience engineering AI systems for reliability, including failure handling, human-in-the-loop controls, guardrails, permissions, monitoring, cost management, and latency management.
- Strong technical judgment on when to use an agent, RAG, classical ML, automation, or conventional software, including discipline to avoid unnecessary complexity.
- Proven ability to work directly with non-technical stakeholders, understand workflow realities, and translate business needs into practical technical solutions.
- Strong product and business judgment to distinguish interesting technical problems from problems worth solving.
- Comfort operating in ambiguous, early-stage environments where defining the problem is part of the role.
- Ability to move between rapid experimentation and production engineering.
Preferred qualifications
- Experience in a forward-deployed, embedded, solutions engineering, applied AI, or highly cross-functional engineering role.
- Experience building tool-using or agentic systems that plan and execute multi-step workflows.
- Experience with APIs, MCP, event-driven systems, enterprise SaaS integrations, and internal data systems.
- Experience building internal platforms, SDKs, reusable tooling, or shared components adopted by other engineering teams.
- Experience evaluating and observing LLM or agent systems, including eval design, tracing, monitoring, and drift detection using tools such as Datadog, LangSmith, Langfuse, or similar platforms.
- Experience designing multi-agent systems, planner/executor architectures, specialized agents, or other orchestration patterns where appropriate.
- Familiarity with workflow analysis and process mapping in complex operational environments.
- Exposure to AI governance, security, privacy, and legal review processes, plus experience in media, entertainment, streaming, consumer technology, or similar operational workflows.
Benefits
- Compensation package including salary plus performance bonus earning potential, paid annually
- Flexible time off policies
- 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 pet-friendly offices
Workplace
- Hybrid workplace setting indicated by #LI-Hybrid tag.
Skills
- Python
- LLM Application Development
- RAG
- Prompt Engineering
- Orchestration
- Tool And Function Calling
- APIs
- Datadog
- LangSmith
- Langfuse
