> Markdown version of [/jobs/ext/4639288-frontier-ai-engineer](https://www.wearedevelopers.com/jobs/ext/4639288-frontier-ai-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Frontier AI Engineer - **Company:** Postman, Inc. - **Location:** New York, NY, United States - **Salary:** $300,000 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Systems Engineering, Software Debugging, Distributed Systems, Regression Testing, Search Technologies, AI Infrastructure, Enterprise Data Management, Postman, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Hallucination Detection, Agentic-AI, AI Platforms, Low Latency, Machine Learning Operations, Automation Anywhere - **Published:** November 8, 2026 - **Apply:** https://www.juju.com/job/28_25196_c6bb366474b7eb00a9f7f137159facc1 ## About the Role We are looking for people who are deeply technical and love building. People who are just as comfortable opening a terminal and debugging an agent trace as they are sitting with another engineering team and working through an architecture. People who understand that building with LLMs is not just about writing prompts. It is about systems engineering, evaluation, reliability, data, APIs, security, and everything required to make probabilistic systems behave predictably in production. People who are curious about where models are going, but equally interested in what it takes to make them useful today. People who like hard problems and do not need everything perfectly defined before they start building. Your background might be in applied AI, agentic systems, AI infrastructure, distributed systems, developer platforms, APIs, security, ML infrastructure, inference, or model platforms. What matters more is how you think and how you build. We care about strong engineering fundamentals, AI fluency, technical depth, curiosity, judgment, speed, and ownership. Above all, we want engineers who want to be close to where AI is actually being built and put into production. If you want to build agents and AI systems that move beyond demos into real production environments, solve hard engineering problems, and help define Frontier AI Engineering at Postman, we would love to talk. ## Description The Opportunity Frontier AI Engineers will work directly with some of our most ambitious customers to design, build, and bring real AI and agentic systems into production. This is a hands-on engineering role. You will write code, build prototypes, debug systems, make architecture decisions, and work side by side with customer engineering teams to get things running. You might be working on an agent that needs reliable tool use, an MCP based integration, a RAG pipeline that needs better retrieval quality, an evaluation framework for production agents, model routing across multiple LLM providers, or the identity and policy layer required to safely let agents take actions. The work spans the full AI stack, from models and inference to agents, APIs, tools, context, evaluation, security, observability, and production infrastructure. Just as importantly, you will bring what you learn back into Postman. We do not want to build a collection of one off solutions. We want to understand the hard AI engineering problems our customers are running into, recognize the patterns that repeat, and turn those lessons into product capabilities that can help thousands of other developers. What You'll Do * Build real AI systems. Work with LLMs, agents, MCP, APIs, tool calling, RAG, vector search, structured outputs, multimodal models, and production AI workflows. * Design agentic systems. Build agents that can reason across tools, APIs, enterprise data, and workflows while maintaining the right controls around identity, permissions, policy, and execution. * Work across the model layer. Experiment with leading foundation models, model routing, inference, prompt and context engineering, fine tuning where it makes sense, and the tradeoffs between quality, latency, cost, and reliability. * Make AI measurable. Build evaluation systems for agents and LLM applications, including task success, tool correctness, hallucination detection, regression testing, safety, latency, and cost. * Make AI production ready. Work on observability, tracing, guardrails, fallback strategies, rate limits, retries, caching, model gateways, and the infrastructure needed to operate AI systems reliably at scale. * Solve the context problem. Help customers reason about retrieval, memory, grounding, context windows, embeddings, vector databases, enterprise data, and how the right context gets to the right model or agent at the right time. * Secure agent actions. Work on authentication, authorization, secrets, API access, policy enforcement, human approval, auditability, and the controls required when AI systems can actually take actions. * Own problems end to end. Start with a problem that may not be clearly defined and take it from architecture to prototype to evaluation to production. * Stay close to the code. We are looking for builders. You will prototype, integrate, debug, experiment, benchmark, and ship. * Work with ambitious engineering teams. Spend time with AI teams, platform teams, developers, and engineering leaders who are pushing the boundaries of what they can build. * Shape what Postman builds next. The problems you see in the field will directly influence our AI platform, APIs, Gateway, identity, agent capabilities, developer experience, and product roadmap. * Turn learning into a product. Identify patterns that repeat across customers and turn them into reusable primitives, reference architectures, workflows, and platform capabilities. * Help define Frontier AI Engineering at Postman. We are early. The first engineers who join will help define the technical bar, architecture, engineering practices, operating model, and culture of the team.