Traba

Staff Software Engineer (Applied AI)

Traba
US San Francisco, CA, US
Onsite 2026-06-23
Announced salary
$240k–$300k
Market rate in San Francisco : $119K - $214K (median $166K) · BLS OEWS 2025

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. **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. **About You:** * **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

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