Inception·Machine Learning Engineer·Technical Phone Screen
Jun 2026
Technical screen for an ML Engineer role at Inception. The main problem was implementing autoregressive text generation in PyTorch from scratch, covering greedy decoding up through nucleus sampling. Pretty dense for a single question but it's the kind of thing you either know cold or you don't.
- Implement an autoregressive text generation function in PyTorch that supports greedy decoding, temperature sampling, top-k sampling, and top-p (nucleus) sampling. The function should stop when a max token budget is hit or when all sequences in the batch have produced an end-of-sequence token.
“This looked manageable at first glance and then I got into the top-p part and my brain started leaking.”