Research Engineer, Machine Learning (Reinforcement Learning)
This is an analysis of the public job description. It is not a confirmed description of this company's interview process, and no company has reviewed or endorsed it.
What the posting clearly emphasises
- Reinforcement learning applied to large language models.
- Agentic tool use, computer use and code generation.
- Distributed systems, training environments, evaluation and performance.
What that likely means for preparation
- Expect systems design in the training sense: data flow, throughput, checkpointing, and where a run stalls.
- Be able to discuss reward design and its failure modes concretely, including reward hacking you have personally seen.
- Performance reasoning — utilisation, memory, communication overhead — is likely to come up in a design conversation.
What stays unknown about the loop
- How deep the loop goes into RL theory versus engineering throughput, and whether a coding exercise is included.
- The round order, count and length. Postings describe the job, not the loop.
- Whether AI assistants are allowed, required or banned in any coding exercise.
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