Agent Systems Engineer
Hybrid (San Francisco)Full-timePosted 2mo ago
About the role
Build agent systems at the core of Adaption’s product, enabling reliable, multi-step execution across real tools and services under real-world constraints (incomplete information, external failures, limited budgets, unpredictable traffic). Own planning, tool use, error recovery, and continuous improvement.
Responsibilities
- 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.
Qualifications
- 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.
Benefits
- Flexible work: In-person collaboration in the Bay Area, a distributed global-first team, and team offsites.
- Adaption Passport: Annual travel stipend to explore a country you've never visited.
- Lunch Stipend: Weekly meal allowance for take-out or grocery delivery.
- Well-Being: Comprehensive medical benefits and generous paid time off.
Last updated Sep 3, 2026. Interested? Apply on the company site.
About Adaption
Everything intelligent adapts. So should AI.
Adaptive DataAdaptive IntelligenceAdaptive InterfacesproductAI that adaptsAI that learnson-the-fly malleable datasetscontinual learning
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