Adaption builds AI systems that adapt as real world conditions change. Moving beyond static and costly retraining cycles, Adaption delivers intelligence that evolves through real world interaction, enabling efficient AI across domains, languages and... Backed by Emergence Capital.
About the role
You'll build the agent systems at the core of our product. These systems turn customer goals into reliable, multi-step execution across real tools and services. This is not about building demos. You'll work on agents that operate under real constraints: incomplete information, external failures, limited budgets, and unpredictable traffic. You'll own how they plan, use tools, recover from errors, and improve over time.
What they're looking for
- 5+ years building production ML or backend systems, including taking LLM or agent applications from prototype to production
- Strong understanding of agent design: planning, reasoning, tool use, orchestration, and memory
- Experience building rigorous evaluation systems, plus execution tracing and observability for agents, with a focus on reproducibility
- Familiarity with the OpenAI Responses API, MCP, and server- versus client-side execution
- Above all, we're looking for great teammates who make work feel lighter and aren't afraid to go out on a limb with bold ideas. You don't need to be perfect, but you do need to be adaptable. We encourage you to apply, even if you don't check every box
More about this role
You'll build the agent systems at the core of our product. These systems turn customer goals into reliable, multi-step execution across real tools and services.
This is not about building demos. You'll work on agents that operate under real constraints: incomplete information, external failures, limited budgets, and unpredictable traffic. You'll own how they plan, use tools, recover from errors, and improve over time.
Design agent architectures for planning, reasoning, tool use, memory, and integration with external systems and data.
Improve reliability on long-running, multi-step tasks, including failure recovery.
Build the loops that let agents improve with real use, so performance compounds instead of staying frozen.
Develop evaluations that measure agent performance and resist being gamed.
Make practical tradeoffs between quality, latency, cost, and complexity.
5+ years building production ML or backend systems, including taking LLM or agent applications from prototype to production.
Strong understanding of agent design: planning, reasoning, tool use, orchestration, and memory.
Experience building rigorous evaluation systems, plus execution tracing and observability for...
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