Sr. Applied AI Engineers

Summary

Multiple early-stage portfolio companies (Seed/Series A) are hiring engineers to build the next generation of AI-native systems across enterprise software, security, healthcare, developer tools, and vertical SaaS.

These companies are building systems that go beyond copilots — software that reasons, takes action, and executes real-world workflows end-to-end. While each company operates in a different domain, they share a common set of technical challenges: goal-driven execution, multi-step reasoning, system reliability, and increasingly, coordination across multiple agents and tools.

This is not a single role at one company. Applicants will be considered for multiple openings across Greylock-backed companies, and we will work directly with you to match you with the most relevant teams based on your experience and interests.

• Design and build agent systems that execute multi-step workflows across real-world environments
• Develop goal-driven systems that plan, act, and adapt in the face of ambiguity
• Contribute to or lead the design of multi-agent architectures, where systems coordinate to complete complex tasks
• Build orchestration layers that manage tool use, decision-making, and long-running workflows
• Define and implement reliability mechanisms including evaluation, guardrails, recovery, and human-in-the-loop systems
• Drive system performance across latency, cost, and quality in production
• Partner with founders, product, and engineering teams to translate open-ended problems into working systems
• Help set technical direction and establish best practices as teams scale

• Orchestration: workflow management, tool routing, multi-component systems
• Execution: integrations with APIs, services, and real-world systems
• State & Memory: persistent context, user/system state, temporal reasoning
• Evaluation & Control: feedback loops, safety systems, observability
• Infrastructure: deployment, scaling, monitoring, and performance optimization

• Experience building and shipping production LLM or agent-based systems that take actions, not just generate outputs
• Strong understanding of goal-driven execution and multi-step workflows
• Experience with orchestration, tool use, or multi-component systems (e.g., ReAct, Lang Graph, or similar approaches)
• Track record of owning systems end-to-end, including reliability and iteration in production
• Comfort working across the stack, from model behavior to system integration
• Strong engineering fundamentals (Python or similar; experience building production systems required)
• Ability to operate in ambiguous environments and make high-leverage technical decisions

• You’ve built systems that complete workflows end-to-end, not just assist users
• You’ve worked on agents that operate over time (stateful, proactive, or long-horizon systems)
• You’ve designed or contributed to multi-agent or multi-component systems
• You’ve owned failure modes, reliability, and recovery in production systems
• You’ve integrated AI systems with external tools, APIs, or enterprise environments
• You’ve built from first principles in early-stage settings