Staff Frontier Agents Engineer (Applied AI)

Space Capital is a company that helps organizations build and deploy reliable production AI applications. They are seeking a Staff Frontier Agent Engineer (Applied AI) to bridge the gap between AI research and production deployment, working directly with enterprise customers to design and deploy intelligent systems that utilize various AI technologies.

Responsibilities:

• Design and deploy production AI agents that leverage the latest advances in large language models, reasoning, retrieval, memory, and tool use
• Architect intelligent systems that combine LLMs, traditional machine learning, structured knowledge, enterprise data, and deterministic software into reliable production workflows
• Engineer customer intelligence layers, retrieval pipelines, memory systems, and knowledge representations that allow agents to reason over large, heterogeneous enterprise data
• Develop multi-agent systems that coordinate reasoning, planning, tool execution, and human oversight
• Translate frontier AI research into production systems by rapidly evaluating new models, prompting techniques, reasoning paradigms, and agent architectures
• Own the full experimentation lifecycle, from hypothesis generation to production rollout
• Design rigorous evaluation frameworks using offline benchmarks, online A/B experiments, golden datasets, regression suites, LLM-as-a-Judge, and human evaluation
• Run controlled experiments and ablation studies to understand the contribution of different models, prompts, retrieval strategies, reasoning techniques, memory systems, and agent architectures
• Continuously evaluate newly released frontier models and determine where they meaningfully improve quality, latency, reliability, or cost
• Develop confidence estimation, reflection, and continuous learning systems that improve agents over time using real-world feedback
• Measure success through business outcomes, not benchmark scores
• Build production-quality AI systems with a strong emphasis on reliability, observability, latency, safety, and cost
• Design agent guardrails, fallback strategies, tracing, monitoring, and evaluation pipelines that enable safe deployment in high-stakes environments
• Collaborate with infrastructure engineers to deploy AI systems securely within enterprise cloud environments
• Build human-in-the-loop workflows that effectively combine AI automation with expert oversight
• Partner directly with enterprise customers to understand their business, data, and operational challenges
• Translate ambiguous customer problems into production AI architectures
• Rapidly prototype new ideas, validate them with customers, and evolve successful solutions into scalable production systems
• Identify reusable patterns that become core capabilities across many enterprise deployments