Technical practice
How would you prototype an LLM-based agent in Python for an open-ended task?
What to coverThe role calls for strong programming skills, preferably in Python, and rapid prototyping with LLMs or AI systems. Suggested preparation: explain how you would define a small testable task, build a working loop, and use early results to decide what to improve.
How would you design planning and memory for a long-horizon agent?
What to coverThe posting names long-horizon reasoning and experiments with planning and memory architectures. Suggested preparation: compare ways to track progress and retain relevant state, including the trade-off between richer context and complexity.
How should an agent use tools and structured or proprietary enterprise data?
What to coverApplied work may involve agents interacting with tools and data systems over complex enterprise workflows. Suggested preparation: discuss how to select and validate tool calls, handle incomplete data, and limit actions when access or results are uncertain.
Where would you put human review in a multi-step workflow agent?
What to coverThe posting mentions human-in-the-loop systems and treats controllability and auditability as important constraints. Suggested preparation: identify decisions that merit review, what context a reviewer needs, and how the agent should resume or stop afterward.
How would you evaluate an agent's planning and tool use?
What to coverThe R&D track includes evaluation frameworks and benchmarks for planning and tool use. Suggested preparation: define representative tasks, success and failure criteria, and how you would distinguish a successful outcome from an unreliable path to it.
How would you investigate a recurring agent failure?
What to coverThe role includes analyzing failure modes and building tools to evaluate or debug agents. Suggested preparation: describe how you would inspect the agent's steps, group failures by cause, test a targeted change, and check for regressions.
How would you assess alignment and interpretability in an agent prototype?
What to coverAlignment, interpretability, transparency, and robustness are explicit areas of work. Suggested preparation: distinguish observable behaviour from claims about why it occurred, and propose checks that reveal unsafe or hard-to-explain decisions.
What would make an agent research harness useful across experiments?
What to coverThe R&D track may build harnesses and reusable research infrastructure. Suggested preparation: explain how a harness could keep tasks, agent runs, and measurements comparable while making failures inspectable.
How would you scope an AI Scientist-style agent for hypothesis generation and experiments?
What to coverThe product and tooling track lists hypothesis generation and experiment execution as possible work. Suggested preparation: describe a bounded workflow with checkpoints for assessing hypotheses and results, rather than assuming autonomous conclusions are reliable.
How would you turn an agent-debugging prototype into a useful tool?
What to coverThe posting calls for tools that help researchers and engineers evaluate or debug agents. Suggested preparation: identify the user's debugging task, the information the tool should surface, and how you would test whether it saves effort without hiding important failures.
Behavioural practice
Tell me about a time you made progress on an ambiguous technical problem.
What to coverThe role emphasizes independently identifying valuable directions in open-ended work. Suggested preparation: use a real situation, your actions to narrow the problem and choose a first experiment, and the result.
Describe a time you collaborated across different technical or product perspectives.
What to coverThe posting calls for collaboration with research, engineering, and product teams. Suggested preparation: give a real situation, how you reconciled differing priorities or feedback, and what resulted.
Tell me about a time technical reading changed your approach.
What to coverReading papers or technical material is an explicit part of the role. Suggested preparation: describe the situation, how you tested or applied what you learned, and the resulting change or finding.
Describe an experiment or prototype that did not work as expected.
What to coverThe work includes experiments, failure analysis, and honest testing. Suggested preparation: explain the situation, the evidence you gathered and actions you took, and what the result taught you.
Tell me about a time your documentation helped others use your work.
What to coverThe role calls for clear documentation of findings and may produce research artifacts or internal tools. Suggested preparation: identify the audience and situation, what you documented, and the result for collaborators.