We’re hiring a Research Engineer to push Console’s core agent loop from powerful to truly self-improving. As more customers rely on Console to automate critical back-office operations, we need engineers who can turn real production traffic into better agents, evals, and specialist models.
This role sits at the intersection of applied AI engineering and research. You’ll build the systems that let us measure, debug, and improve agent behavior in real-world conditions: production traces, offline replay, labeling workflows, eval harnesses, prompt and program optimization, and fine-tuning loops for high-volume agent tasks.
One of your first major focus areas will be improving how Console’s agents reason over complex enterprise context. Our agents need to understand users, apps, devices, tickets, licenses, policies, and customer-specific data well enough to answer questions and take action reliably. You’ll help turn this into a compounding optimization loop: using production traces, evals, assisted labeling, prompt/program optimization, and targeted model adaptation to make the system measurably better over time.
You’ll work closely with product, engineering, and leadership to ship improvements into production quickly, while helping define what research at Console looks like as we scale.