{"id":2063002,"url":"https://alion.io/job/crunchyroll-news-forward-deployed-engineer","title":"Forward Deployed Engineer","company":{"id":40459,"name":"Crunchyroll","domain":"crunchyroll.com","url":"https://alion.io/company/crunchyroll-news","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Welcome to the Jungle","truth_index":null},"role":"Solutions","role_family":"Solutions","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Los Angeles, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":183400,"max":229200,"currency":"USD","period":"year","gross":null,"usd_annual":229200},"salary_estimate":null,"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Context Engineering","optional":false},{"name":"Datadog","optional":false},{"name":"Function Calling","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Langfuse","optional":false},{"name":"LangSmith","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Tool Use","optional":false}],"status":"live","first_seen_at":"2026-10-08T00:08:07Z","employer_posted_date":"2026-10-08","last_verified_at":"2026-10-12T13:54:42Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"Join Crunchyroll as a Forward Deployed Engineer, where you'll work closely with teams across the organization to build AI solutions that enhance productivity and efficiency. You'll embed with teams, understand their workflows, and identify opportunities for AI and automation. This role requires strong business judgment, consultative problem-solving skills, and hands-on engineering experience. You'll be part of the AI Enablement team in the Executive Office, focusing on high-value business problems and practical AI-powered solutions.\nMissions\nIdentifying and understanding workflows to determine where AI or automation can create meaningful value.\nRapidly building solutions alongside the teams who will use them, taking problems from ambiguity through prototype, production, and adoption.\nWorking closely with various partners to ensure solutions are scalable, secure, and maintainable, and leaving behind reusable components and lessons.\nProfil recherché\n - Experience engineering AI systems for reliability, including failure handling, human-in-the-loop controls, guardrails, permissions, monitoring, cost management, and latency management- Demonstrated experience designing, building, and deploying AI systems that interact with APIs, enterprise systems, data sources, or external tools to complete multi-step tasks\n- Experience taking AI solutions end-to-end, from an ambiguous problem statement through architecture, prototyping, implementation, deployment, and iteration\n- Strong product and business judgment. You can distinguish an interesting technical problem from a problem worth solving\n- The ability to move between rapid experimentation and production engineering, knowing when speed matters and when reliability, security, and scale matter more\n- Proven ability to work directly with non-technical stakeholders, understand how their work actually happens, and translate business needs into practical technical solutions\n- 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\n- Strong technical judgment on when to use an agent, RAG, classical ML, automation, or conventional software, including the discipline to avoid unnecessary complexity\n- Comfort operating in ambiguous, early-stage environments where you are expected to define the problem as much as solve it\n- 6+ years of software engineering experience, including significant recent experience building and shipping applied AI or LLM-powered systems in production\n- Experience building tool-using or agentic systems that plan and execute multi-step workflows\n- Experience in a forward-deployed, embedded, solutions engineering, applied AI, or highly cross-functional engineering role\n- Experience with APIs, MCP, event-driven systems, enterprise SaaS integrations, and internal data systems\n- Experience building internal platforms, SDKs, reusable tooling, or shared components adopted by other engineering teams\n- Experience designing multi-agent systems, planner/executor architectures, specialized agents, or other orchestration patterns where they are appropriate\n- 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\n- Exposure to AI governance, security, privacy, and legal review processes, including risks related to data access, permissions, prompt injection, tool use, autonomous actions, and sensitive information\n- Familiarity with workflow analysis and process mapping, particularly in complex operational environments\n- Experience in media, entertainment, streaming, consumer technology, or another environment with complex content and operational workflows. 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