Staff Software Engineer (Agents)

Traba•San Francisco, CA

7d•$240,000 - $300,000•Hybrid

About The Position

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 seeking an entrepreneurial Staff Applied 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. 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.

Requirements

7+ years of professional software engineering experience, with 2+ years of hands-on production work on LLM- or agent-based systems.
Deep proficiency in Python and/or TypeScript/Node.js, and a strong 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, cost/latency tuning, and the operational lessons that come with all of it.
Background that maps to at least one of: vertical AI / AI-agent company, Palantir/Scale-style FDE, or an AI-native data company (Hex, Omni, dbt). Bonus for supply chain, logistics, or industrial operations exposure.
A history of leading 0-to-1 product builds in early-stage environments—comfortable with ambiguity, pragmatic about tradeoffs, 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—often in the same week.
A genuine excitement about industrial operations and the chance to use applied AI to make them dramatically better.

Nice To Haves

Ideal: prior experience at a vertical AI or AI-agent company (Nash, HappyRobot, Augment, Pallet, Harvey, Legora, ElevenLabs), Palantir/Scale-style FDE, or an AI-native data company (Hex, Omni, dbt).
Bonus for supply chain, logistics, or industrial operations exposure.

Responsibilities

Architect Traba's agent platform end-to-end—the orchestration runtime, the eval and observability stack, the integration layer to internal services and customer systems (WMS/TMS/ERP), and the patterns that every agent we ship is built on.
Own the foundational technical decisions for this part of the business: model strategy, agent harness design, retrieval and memory architecture, tool/MCP surface, and how we measure quality.
Spend real time in the field with customers and operators. Translate what you see into durable product and codify repeatable deployment patterns so each rollout compounds on the last.
Build evaluation as a real engineering discipline—datasets, graders, regression suites, experimentation tooling—so we ship agent improvements with the same confidence we'd ship backend code.
Hire and mentor the engineers who will build alongside you. Set the technical standards that define what “good” looks like for applied AI at Traba.
Partner with the CTO, product, and ops leadership on the multi-year platform roadmap. Think ahead to what the company and the agent layer will need a year or two from now, and start building it today.