[Hiring] AI Researcher @Bolter

Role Description

As Bolter's AI Researcher, your job is to make agents that do real work trustworthy, capable, and useful - and to figure out how before we build it at scale. This is applied research with direct product impact.

• Design and run experiments to test how Bolter's agents behave - reliability, context retention, multi-step task completion, and where they fail.
• Build and own the evaluation layer.
• Design evals that measure whether agents actually do the work, not just whether they sound plausible.
• Research the frontier to keep Bolter current on the state of the art in LLM agents, tool use, and reliability - and translate that into what we should build.
• Turn findings into decisions by producing clear, actionable recommendations the engineering team can ship, not just papers.
• Prototype research into product by taking promising ideas from experiment to working prototype, and hand off what proves useful.
• Shape the research roadmap as Bolter grows.

Qualifications

• A track record of applied AI/ML research - ideally in LLM application design, agentic systems, evals, or production AI reliability.
• Strong experimental design and analysis. You know how to test a hypothesis rigorously and read the result honestly.
• Strong engineering fundamentals - you can prototype your own experiments, not just direct others to run them.
• Deep familiarity with LLMs, tool use, context management, and the failure modes of agentic systems.
• Strong written communication. Research that isn't understood and acted on doesn't help.
• You're rigorous but pragmatic. You know when a finding is strong enough to act on and when it needs more evidence.
• You're comfortable with ambiguity. Many of the problems we're solving don't have textbook answers.
• You can leverage AI agents as a force multiplier - we run lean, with a small human core augmented by a fleet of specialised agents.
• You care about craft, but you ship.

Bonus Points

• Experience with LLM evals, hallucination mitigation, or production AI reliability at scale.
• Experience building or evaluating agentic systems, tool use, or autonomous workflows.
• A public track record - papers, open source, blog posts, side projects. We love researchers who ship outside work too.
• Experience in product-led research - where the output is a shipped feature, not just a finding.