let me cut through the vague stuff I've seen posted about Anthropic MLE interviews. I just went through the full loop for an MLE role on their alignment research infrastructure side. here's what they actually asked.
phone screen: one ML conceptual question, one coding problem. the ML question was about training instabilities: you're running a large language model training run and you notice loss spikes every ~500 steps. walk me through how you'd diagnose it. they weren't looking for one right answer, they wanted to see systematic debugging: learning rate scheduler? gradient clipping? data quality in that batch window? mixed precision overflow?
the coding problem was on the simpler end. clean implementation of a custom loss function in PyTorch, make sure it handles edge cases and is numerically stable. they cared about whether I'd think about float precision without being prompted.
onsite, 5 rounds (yes, 5): ML depth: deep on transformers. not just "explain attention" but: what changes when you scale context length, why does KV cache memory scale quadratically, how do you think about grouped-query attention as a tradeoff. if you're not up to date on the architecture space, this will sting. systems for ML: distributed training design. they gave me a training job that doesn't fit on one node and asked how I'd approach data parallelism vs. model parallelism vs. pipeline parallelism. also: what fails first as you add nodes. coding: more involved than the phone screen. implement something meaningful, not just a code kata. mine involved building a small evaluation harness for a text generation model. research communication: explain a paper you've read recently and what you took from it. I chose a mechanistic interpretability paper. they engaged substantively, which was a good sign. mission/values: the standard Anthropic round. for MLE it had an ML safety flavor. how do you think about evaluation of models for harmful outputs? what's the difference between capability and safety evaluation?
the bar is genuinely high and they care about the AI safety angle more than any other MLE interview I've done. if you're applying here because it's a hot AI company and not because you have any interest in the safety mission, it'll show.
offer was around 240-290k TC depending on your equity assumptions. haven't decided whether to take it yet.