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Traba is hiring for a platform-engineering role on the founding team in New York. You’ll shape the architecture and own core backend services, APIs, and data models to run AI-assisted staffing at scale across millions of shifts.
The role emphasizes real-time matching, autonomous vetting pipelines powered by ML, and a commitment to performance, reliability, and scale in a fast-growing startup.
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.
You’ll build Traba’s product on top of AI agents—and set the standard for how we do it. You'll put frontier models and existing agents to work across our staffing marketplace to automate the pipeline that sources, vets, matches, and places workers on millions of shifts—and own the core backend services and platform it all runs on. (Our AI Agents team builds the agent platform; you make it real across the stack.) This is a platform-engineering role first, so you'll go where the priorities are: the infrastructure an agent needs one quarter, a matching, data, or scaling problem the next. You'll join the founding team, partner with our CTO on key architectural decisions, iterate on the roadmap, and build the foundational platform that scales over the next several years.
An applier who sets the bar. You wire frontier models and existing agents into real product across the stack—and set the standard for how the team does it. You don't need to own the orchestration internals; you make them real in production, and you know which corners are safe to cut at an early stage.
Voracious learner. You move easily from API design to deployment infrastructure.
Deep full-stack experience. You've designed performant, scalable applications with real expertise in APIs, data modeling, query optimization, and distributed systems.
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.
Traba is dedicated to promoting Equal Opportunity employment practices. We evaluate all applicants without discrimination based on race, color, religion, creed, national origin, age, sex, gender, marital status, sexual orientation and identity, genetic information, veteran status, citizenship, or any other characteristics that are legally protected by local, state, or federal regulations. We encourage applicants from a variety of backgrounds, experiences, and skill sets.
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 industry’s problems.
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