greenhouse
Enterprise AI Systems Engineer
Juul Labs
Remote, USA
Full-time
Senior · 5+ years experience
Remote
USD 126000-206000 yearly / year
Discovered Yesterday
ClaudeGeminiOpenRouterCursorNotebookLMMCP
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THE COMPANY
Juul Labs's mission is to transition the world’s billion adult smokers away from combustible cigarettes, eliminate their use, and combat underage usage of our products. We have the opportunity to address one of the world’s most intractable challenges through a commitment to exceptional quality, research, design, and innovation. Backed by leading technology investors, we are committed to the same excellence when it comes to hiring great talent.
We are a diverse team that is united by this common purpose and we are hiring the world’s best engineers, scientists, designers, product managers, operations experts, and customer service and business professionals. If the opportunity to build your career is compelling, read on for more details.
ROLE AND RESPONSIBILITIES
- You own these platforms end to end. What you build gets used across the whole company, and you decide how AI runs here rather than inheriting someone else's design.
- Administer our enterprise Claude, Gemini, OpenRouter, Cursor and NotebookLM tenants. User provisioning, groups and roles, workspace configuration, and tenant security settings.
- Build and maintain the content that makes those platforms useful: skills, prompts, projects, connectors, and the documentation behind them. Drive adoption across 250 users and growing.
- Track consumption and spend across all three platforms. Report on usage, find idle seats, and support the business-unit chargeback model.
- Keep the platforms running. Availability monitoring, feature rollouts, user issue triage, and escalation ownership when something breaks.
- Build and operate the MCP servers and gateways that connect these platforms to internal systems, with least-privilege access and full audit logging.
- Run the LLM gateway that centralizes routing, authentication, rate limiting, logging, and policy enforcement across model providers.
- Choose the right model for each workload, commercial or open-weight, based on cost, latency, capability, and how sensitive the data is.
- Build agentic workflows and automations against internal APIs and tools, with the evaluation and guardrails to keep them predictable.
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