Traba
AI operating layer for the industrial supply chain
StaffSoftwareEngineer(Agents)
Neural analysis suggests this role is
optimal for Lead candidates.
“Staff Software Engineer (Agents) at Traba. Skills: Architect Traba's agent platform end-to-end, Own the foundational technical decisions for this part of the business, Spend real time in the field with customers and operators, Build evaluation as a real engineering discipline, Hire and mentor the engineers who will build alongside you, Partner with the CTO, product, and ops leadership on the multi-year platform roadmap. lead the development of Traba's agentic platform. synthesizes the data flowi”
What You'll Achieve.
make the core architectural calls on how agents are built, evaluated, and deployed; set the bar for quality; bring the outside perspective on agent-building, FDE-style customer immersion, and data-product craft that this 0-to-1 product needs; architect Traba's agent platform end-to-end; Own the foundational technical decisions for this part of the business; Translate what you see into durable product; 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; Set the technical standards that define what “good” looks like for applied AI at Traba; Think ahead to what the company and the agent layer will need a year or two from now, and start building it today
Industry & Context.
synthesizes the data flowing through our marketplace; talks to our customers' operational systems; acts autonomously inside the workflows that run their facilities; designing distributed systems, APIs, and data models; ownership of a non-trivial production agent system; operational lessons that come with all of it; pragmatic about tradeoffs
lived with the on-call, Spend real time in the field with customers and operators
What They're Looking For.
Must Have
7+ years of professional software engineering experience, 2+ years of hands-on production work on LLM- or agent-based systems, Deep proficiency in Python and/or TypeScript/Node.js, 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), A history of leading 0-to-1 product builds in early-stage environments, written and verbal communication
Nice to Have
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), supply chain, logistics, or industrial operations exposure, comfortable with ambiguity, pragmatic about tradeoffs, and high-agency by default
What You'll Do.
lead the development of Traba's agentic platform
synthesizes the data flowing through our marketplace
talks to our customers' operational systems
acts autonomously inside the workflows that run their facilities
make the core architectural calls on how agents are built
set the bar for quality
Architect Traba's agent platform end-to-end
Own the foundational technical decisions for this part of the business
Spend real time in the field with customers and operators
Translate what you see into durable product
codify repeatable deployment patterns
Build evaluation as a real engineering discipline
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
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
How You'll Work.
Team & Collaboration
partner with our CTO; partner with the CTO, product, and ops leadership; bring others along
Communication Scope
written and verbal communication; run a customer workshop; write the design doc; recruit your future teammates
Process & Methodology
leading 0-to-1 product builds
Full Job Description
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. 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. ABOUT YOU - You've already built agents that survived contact with reality. You've shipped agent systems into production at meaningful scale—designed the harness, picked the orchestration patterns, owned the evals, and lived with the on-call. You have strong opinions on where to draw the line between prompting, fine-tuning, retrieval, and code, and why. - Domain depth meets technical breadth. You're as comfortable in a warehouse on a cus
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