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 Staff Agent Engineer to join as a founding member of the Agents team and lead the development of Traba's agentic platform-the layer that synthesizes the data flowing through our marketplace, talks to our customers' operational systems, and acts autonomously inside the workflows that run their facilities. (Our Applied AI team puts these agents to work across the product; you own the agents, evals, and model strategy they depend on.) You'll partner with our CTO to make the core architectural calls on how agents are built, evaluated, and deployed at Traba; set the bar for quality and reliability; and bring the outside perspective on agent-building, FDE-style customer immersion, and data-product craft that this 0-to-1 product needs.
About You- You've built agents that survived contact with reality. You've shipped agent systems into production at scale-designed the harness, picked the orchestration patterns, owned the evals, and lived with the on-call-and you have strong opinions on where to draw the line between prompting, fine-tuning, retrieval, and code.
- Domain depth meets technical breadth. You're as comfortable in a warehouse on a customer site as in a design doc-you learn an industry's actual operations (WMS quirks, shift cadence, exception handling) and let that shape architecture.
- Set direction by shipping. You raise the bar by writing the canonical example, not just the doc-picking the foundational tools, integrating the right model providers, designing the eval infrastructure, and bringing others along.
- Sweat the small stuff at staff scale. You have strong opinions on eval datasets, prompt versioning, observability for agents, and the line between a clean abstraction and an over-engineered one.
You Will- Architect Traba's agent platform end-to-end-orchestration runtime, eval and observability stack, the integration layer to internal and customer systems (WMS/TMS/ERP), and the patterns every agent is built on.
- Own the foundational technical decisions: model strategy, harness design, retrieval and memory architecture, tool/MCP surface, and how we measure quality.
- Spend real time in the field with customers and operators-translating what you see into durable product and repeatable deployment patterns.
- Build evaluation as a real engineering discipline-datasets, graders, regression suites, and experimentation tooling.
- Hire and mentor the engineers who build alongside you, and set the standard for what "good" looks like.
- Partner with the CTO, product, and ops leadership on the multi-year platform roadmap.
You Have- 7+ years of software engineering, with 2+ years of hands-on production work on LLM- or agent-based systems.
- Deep in Python and/or TypeScript/Node.js, with a track record designing distributed systems, APIs, and data models on PostgreSQL and modern messaging (Kafka, RabbitMQ, or equivalent).
- Demonstrated ownership of a non-trivial production agent system: orchestration, tool use, retrieval, evals, observability, and cost/latency tuning.
- Background that maps to at least one of: vertical AI / AI-agent company, a forward-deployed engineering role, or an AI-native data company. Bonus for supply chain, logistics, or industrial exposure.
- A history of leading 0-to-1 builds in early-stage environments-comfortable with ambiguity and high-agency by default.
- Strong written and verbal communication-you can run a customer workshop, write the design doc, and recruit your future teammates.
Benefits- Start-up equity
- Competitive Salary
- 100% Paid health, dental & vision coverage
- 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 Health
Salary Range DetailsThe compensation range for this position is set between $240,000 and $300,000, 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.
