Staff Software Engineer (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., * 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.

Do you have experience in Workshop facilitation?, * 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 customer site as you are in a design doc. You learn an industry’s actual operations-WMS quirks, shift cadence, exception handling-and let that knowledge shape architecture decisions. 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).
• Set direction by shipping. You raise the engineering bar by writing the canonical example, not just the doc. You pick the foundational tools, integrate the right model providers, design the eval infrastructure-and you bring others along.
• Sweat the small stuff at staff scale. You have strong opinions on design patterns, eval datasets, prompt versioning, observability for agents, and the difference between a clean abstraction and an over-engineered one. You understand that how we do one thing is how we do everything., * 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.

Pulled from the full job description

• Health insurance
• Vision insurance
• Dental insurance
• Commuter assistance
• Snacks provided, * 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, The 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.

Dream 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’ problems