Research Engineer / Scientist, Personal AGI (Personalization)
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
- Memory and personalization as research problems.
- Reinforcement learning, datasets and evaluations.
- User signals and human data feeding product-driven research.
What that likely means for preparation
- Expect to reason about measuring personalization without a clean label: proxy metrics, user signal quality, and how a metric can be gamed by the model.
- Memory architecture questions are plausible: what to store, when to forget, and how retrieval of personal context interacts with safety.
- Have a position on human data pipelines: who labels, quality control, and disagreement.
What stays unknown about the loop
- Whether the loop leans research-scientist (methods, prior work) or research-engineer (systems, throughput).
- 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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