Interview Stories
How to translate the deep technical work in this repository into compelling narratives for top AI research labs.
Anthropic
Anthropic's hiring page says to put independent research at the top. CodeSage is independent research — with a benchmark contribution.
What they'll ask: "What did you learn about where fine-tuning fails?"Answer with catastrophic forgetting, quantization noise, and overconfidence on OOD languages.
Strategy:Connect: "The logprob confidence threshold I built is a form of model uncertainty quantification — the same problem Anthropic works on for Constitutional AI."
OpenAI
OpenAI interviews focus on production systems thinking and scale.
Strategy:Lead with: the vLLM architecture decision — specifically why PagedAttention was the right choice and what 24x means operationally. Then: Modal vs AWS cost analysis ($0.18 vs $5.00 per 1M tokens).
They'll ask: "How would you scale to 10,000 concurrent users?"Answer: tensor parallelism across 4x H100s, prefix caching, request prioritization, async prefill/decode disaggregation.
DeepMind
DeepMind asks for research-adjacent thinking and scientific rigor.
Strategy:Lead with: the W&B experiment comparison — three runs, documented findings, the DoRA vs standard LoRA ablation (+1.5pp).
They'll ask: "How did you decide on r=16?"Answer: "r=32 was +0.3pp at 2.1x cost. Diminishing returns suggest low intrinsic dimensionality — code completion may not require high-rank adaptation because the base has strong code priors."