Product Engineer (Senior/Staff)
Ai ml · Posted 3 months ago
You will build Traba's product on top of AI agents and set the standard for how we do it. You will 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, then own the core backend services and platfo...
- Way of working
- On site
- Location
- San Francisco, CA, New York, NY
- Pay range
- $200,000 to $300,000
- Level
- Staff
- Experience
- 5 to 10 years
- Type
- Full time
- Visa sponsorship
- Not offered for this role
- The company
- Technology,Information and Internet, Information and Internet · 50 to 200 people
Skills that matter here
The full description
Applied AI
You will build Traba's product on top of AI agents and set the standard for how we do it. You will 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, then own the core backend services and platform it all runs on. The AI Agents team owns the agent platform itself; your job is to make it real across the product. This is a platform-engineering role, so priorities will shift: infrastructure one quarter, matching or scaling problems the next. You will join the founding team, partner with our CTO on key architectural decisions, and build the platform that the company runs on for the next several years.
About You
You wire frontier models and existing agents into production across the stack. You do not need to own the orchestration internals, but you know how to make them real, and you know which corners are safe to cut early on. You move easily from API design to deployment infrastructure, and you have designed performant, scalable systems with real expertise in APIs, data modelling, query optimisation, and distributed systems.
You Will
- Define how we apply agents across the stack to automate the staffing pipeline, and how the team builds with AI-assisted tooling.
- Architect and lead core systems, from real-time matching to autonomous vetting pipelines powered by ML and agents.
- Own platform architecture with an eye on what the company will need a year or two out.
- Drive backend performance, reliability, and scale.
AI Agents (Neo AI)
You will build the AI agents themselves: the harnesses, evals, orchestration, and model strategy that the rest of Traba's product depends on. We are looking for a Staff Agent Engineer to join as a founding member of the Agents team and lead development of Traba's agentic platform – the layer that synthesises data flowing through our marketplace, integrates with customers' operational systems, and acts autonomously inside the workflows that run their facilities. The Applied AI team puts these agents to work across the product; you own the agents, evals, and model strategy they depend on. You will partner with our CTO on the core architectural decisions around how agents are built, evaluated, and deployed at Traba, and bring the technical depth and customer immersion this product needs at this stage.
About You
You have shipped agent systems into production at scale. You have designed the harness, chosen the orchestration patterns, owned the evals, and carried the on-call. You have strong opinions on where to draw the line between prompting, fine-tuning, retrieval, and code. You are as comfortable on a customer site as in a design doc, and you let what you see in the field shape architecture. You raise the bar by writing the canonical example: picking the foundational tools, integrating the right model providers, designing the eval infrastructure. You have strong opinions on eval datasets, prompt versioning, and observability for agents, and a good instinct for where a clean abstraction tips into over-engineering.
You Will
- Architect Traba's agent platform end to end: orchestration runtime, eval and observability stack, the integration layer to internal and customer systems (WMS/TMS/ERP), and the patterns every agent is built on.
- Own the foundational technical decisions: model strategy, harness design, retrieval and memory architecture, tool/MCP surface, and how we measure quality.
- Spend time with customers and operators in the field, translating what you see into durable product and repeatable deployment patterns.
- Build evaluation as a real engineering discipline: datasets, graders, regression suites, and experimentation tooling.
- Hire and mentor the engineers who build alongside you.
- Partner with the CTO, product, and ops leadership on the multi-year platform roadmap.
Neo Further Reading
- https://traba.work/neo
- https://traba.work/resources/case-studies/how-shipsmarter-put-its-operation-on-autopilot-with-neo
- https://finance.yahoo.com/technology/ai/articles/traba-launches-neo-ai-decides-191600907.html
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