Traba Senior Software Engineer (Applied AI) at Traba builds AI agents and infrastructure for automating industrial supply chain workflows. The role involves embedding with customers to design and ship production agent harnesses on frontier LLMs.
Responsibilities
Embed with customers and operators to understand how supply chains run today—then design and ship agents that take meaningful work off their plate.
Build production agent systems on frontier LLMs: tool use, sub-agents, retrieval, structured outputs, MCP servers, and the orchestration that ties them together.
Own evaluation as a first-class discipline—datasets from real traces, rubrics and graders, experiments, and improvements you can prove move the needle.
Architect the data, services, and APIs the agent layer depends on—integrating our internal systems with customers' WMS, TMS, and ERP environments.
Codify repeatable deployment patterns so each new customer rollout is faster than the last.
Qualification
Builder with an AI operator's instinctDomain-immersedHigh-agency in ambiguitySweat both ends of the stack5+ years of software engineeringStrong in Python and/or TypeScript/NodeHands-on with the modern agent stackA track record of building in fast
Required
Builder with an AI operator's instinct. You've shipped real product on top of LLMs—not just chat wrappers. You've designed agent harnesses, structured tools, written evals, and tuned prompts against production traces. You think in capability, reliability, and unit economics—not just whether the model says the right thing.
Domain-immersed. You enjoy time with the people who do the work—learning an industry's vocabulary, edge cases, and operational tempo—and let that shape what you build.
High-agency in ambiguity. Dropped into a fuzzy customer problem with a half-formed hypothesis and a deadline, you scope, build, evaluate, and ship without waiting for a spec.
Sweat both ends of the stack. You move between prompt iteration, eval design, backend services, and customer-facing UI in the same week, and you care about evals that catch regressions and traces that are easy to debug.
5+ years of software engineering, with 1+ year shipping LLM- or agent-based features into production.
Strong in Python and/or TypeScript/Node.js, and comfortable designing APIs, distributed systems, and data models in PostgreSQL.
Hands-on with the modern agent stack: production-scale prompt engineering, evaluation frameworks, orchestration patterns, and frontier model APIs.
A track record of building in fast, ambiguous environments—ideally at a vertical AI, AI-agent, forward-deployed, or data-product company.
Excellent written and verbal communication—you can run a customer workshop in the morning and write a clean design doc in the afternoon.
