Software Engineer (AI Agents)

Traba is the AI operating layer for the industrial supply chain.We started in workforce—temp staffing, the biggest operational pain point for the manufacturing and logistics customers we serve—and used it to embed ourselves inside their daily operations and create a far better customer experience through technology.Now those same customers are pulling us beyond staffing into the broader operational workflows that run their facilities. That foundation gave us proprietary data from millions of shifts and deep enterprise relationships.But our edge is more than data: by connecting to the systems running across every facility and activating the workers already on our platform to execute against them, we are building applied AI that drives real productivity gains and transforms how the global supply chain operates at scale.We are backed by Founders Fund, Khosla Ventures, and General Catalyst.You'll build the AI agents themselves—the harnesses, evals, orchestration, and model strategy the rest of Traba's product runs on. We're seeking an entrepreneurial AI Agent Engineer to join as a founding member of the Agents team and build the next layer of Traba's product—an agentic platform that synthesizes the data flowing through our marketplace and operates autonomously inside our customers' supply chain workflows. (Our Applied AI team puts these agents to work across the product; you own the agents and infrastructure they depend on.) You'll embed deeply in our customers' operations, design and ship production agent harnesses on top of frontier models, and turn what you learn in the field into durable product. You will partner closely with our CTO, product, and our operators to take 0-to-1 ideas to scaled deployments.About YouBuilder 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.You WillEmbed 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.You Have1+ years of software engineering experience, with a track record of shipping LLM- or agent-based features to 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.Benefits Start-up equity Competitive Salary 100% Paid health, dental & vision coverage️ Dinner Provided via DoorDash, free DashPass & stocked kitchen for NY employees Commuter benefit Gympass Benefit Additional: One Medical Membership, Gympass, HSA via Optum, Talkspace, HealthAdvocate, Teledoc HealthSalary Range DetailsThe compensation range for this position is set between $140,000 and $200,000, reflecting our market analysis and other relevant considerations. However, exceptions may be made for candidates with qualifications that significantly differ from those outlined in the job description.Equal Opportunity EmployerTraba is dedicated to promoting Equal Opportunity employment practices. We evaluate all applicants without discrimination based on race, color, religion, creed, national origin, age, sex, gender, marital status, sexual orientation and identity, genetic information, veteran status, citizenship, or any other characteristics that are legally protected by local, state, or federal regulations. We encourage applicants from a variety of backgrounds, experiences, and skill sets.Our ValuesDream Big - We are on a path to change the world for the better. We create and communicate a bold direction that inspires a life-changing vision. We don’t sacrifice long-term value for short-term results.Olympian’s Work Ethic - Changing the world never comes easy. We work harder, longer, and smarter, not just two out of three. We put everything we have on the field.Growth Mindset - We confront the toughest challenges head-on and persevere. Sometimes we fail, but we brush ourselves off, adapt, learn, and push forward with resilience.Customer Obsession - We go the extra mile for our workers and businesses. We remain focused on delivering high-quality products and services that solve this massive and overlooked industries’ problemsJob Applicant Privacy NoticeCompensation Range: $140K - $200KLocationNew York City, NYEmployment TypeFull timeDepartmentEPDEngineeringCompensation$140K – $200K • 0.04% – 0.12%We bias strongly toward equity, as generational wealth and extreme ownership mentality is optimized when individuals are made owners