ashby
AI Engineer Trainee
Rentman
Toronto, CAN
Part-time
Entry
Hybrid
Discovered 3 weeks ago
ClaudeBigQueryPythonJavaScriptAPIsgit
Free
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ClaudeBigQueryPythonJavaScriptAPIsgit
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About us
Behind every festival, tour, and shoot you've seen, there's a team making sure the right gear shows up, the crew knows where to be, and the schedule holds together. That's what Rentman makes possible: an operations management platform built to support event & media production teams in 70+ countries.
We're based in Utrecht (NL) with a second office in Toronto (CA). Our 100+ team members work in a culture that's open, flat, international and inclusive, where you'll have real ownership over your work and the freedom to challenge ideas, not just agree with them. We're still a scale-up, with ambitious plans ahead (more on that below), so joining now means your work will genuinely shape what Rentman becomes next. Check out our public roadmap to see what’s coming!
Mission of this role
This traineeship is a genuine way into engineering. You'll have real ownership, with coaching alongside you the whole way, and the possibility to grow further and develop your career at Rentman. You'll join our small AI squad and work directly with the team lead.
You'll build the technologies that make our AI projects reliable, measurable, and worth using: evals, context stores, workflows, agents, and tracing. You'll work hands-on with Claude, our agent platform, and the BigQuery warehouse. You'll always reach for the simplest tool that solves the problem, whether that's a skill, a workflow, or a full agent, with the model that does the job. What you leave behind, the team can run without you.
Curiosity is what drives this work. If you're as intrigued by AI as we are and want to learn, grow, and put your skills to the test, this traineeship is for you. You'll own what you build, from the first design choice to the finished product.
What you'll do
Build. Skills, prompts, workflows, and agents. Only reaching for more when the problem actually needs it.
Define eval sets, so we know whether an agent is actually getting better or just different.
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