about 2 months ago
Responsibilities
- Develop secure execution environments for agent-generated code.
- Manage identity, authentication, and trust boundaries for agents.
- Orchestrate model routing across different model types and environments.
- Implement rate limiting, quotas, and resource management for agent workflows.
- Create state management, memory, and filesystem abstractions for agents.
- Turn emerging ML research ideas into production-ready infrastructure.
- Build core platform capabilities for execution, storage, and state management.
- Prototype and evaluate new technologies for production readiness.
- Collaborate with research teams to shape infrastructure for future agent systems.
Requirements
- Experience building production ML infrastructure with strong systems fundamentals.
- Hands-on work with agentic systems or multi-agent workflows.
- Familiarity with model routing and LLM provider frameworks.
- Experience with scalable, fault-tolerant distributed systems and Kubernetes.
- A track record of quickly prototyping and making productionization decisions.
Benefits
- An open and inclusive culture and work environment.
- Weekly lunch stipend, in-office lunches, and snacks.
- Full health and dental benefits, including a mental health budget.
- 100% Parental Leave top-up for up to 6 months.
- Personal enrichment benefits for arts, culture, fitness, and workspace improvement.
- Remote-flexible work options with offices in major cities.
- 6 weeks of vacation (30 working days).