Traba

Industrial Supply Chain

StaffSoftwareEngineer(AIAgents)

$240–300k New York, New York, United States FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Lead candidates.

The Brief

“Staff Software Engineer (AI Agents) at Traba. Skills: Agent platform architecture, Agent system production, Applied AI. Architect agent platform end-to-end. Own foundational technical decisions”

What You'll Achieve.

Drive real productivity gains; Transform how global supply chain operates at scale; Ship agent improvements with confidence

Industry & Context.

Industrial Supply Chain
Problems you'll solve

Synthesizes data; Acts autonomously; Solves operational pain points; Drives productivity gains; Transforms supply chain operations

Eligibility Requirements

On-call, FDE-style customer immersion

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, Deep proficiency in TypeScript/Node.js, Track record designing distributed systems, Track record designing APIs, Track record designing data models on PostgreSQL, Track record designing data models on modern messaging (Kafka, RabbitMQ, or equivalent), Demonstrated ownership of a non-trivial production agent system, 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), 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, AI-native data company (Hex, Omni, dbt), Supply chain exposure, Logistics exposure, Industrial operations exposure

What You'll Do.

Architect agent platform end-to-end

Own foundational technical decisions

Spend time in field with customers

Translate observations into product

Codify repeatable deployment patterns

Build evaluation as engineering discipline

Hire and mentor engineers

Set technical standards

How You'll Work.

Team & Collaboration

Partner with CTO; Partner with product leadership; Partner with ops leadership; Bring others along

Communication Scope

Written communication; Verbal communication; Run customer workshop; Write design doc; Recruit teammates

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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