this is the post i wish existed when i was prepping. i passed the HF MLE loop in february 2026 and accepted. sharing specifics.
role context: the team works on model evaluation infrastructure, specifically building systems for running benchmark suites at scale across the hub. so it's not pure research, it's applied ML with real engineering constraints.
round 1: ML fundamentals (60 min) this felt like a senior ML screen, not a research screen. they tested breadth, not depth. questions spanned: explain the trade-offs between autoregressive and masked language models for different downstream tasks. you're running an eval suite and you see model A beats model B on MMLU but B beats A on human preference. how do you interpret that? a fine-tuned model degrades on the base task after RLHF. what are the likely causes and how do you debug?
no "implement backprop from scratch" type stuff. they want to see you can think clearly about real problems.
round 2: coding (90 min) two problems. first was an algorithm problem (tree traversal variant), medium difficulty. second was more ML-adjacent: implement a simple evaluation harness that can run a model on a dataset and compute multiple metrics, handling potential errors in individual examples without stopping the whole eval. that second one is very HF-flavored and good to think through ahead of time.
round 3: ML system design (60 min) design a system to run model benchmarks continuously as new model versions are uploaded to the hub. the interesting constraints: compute is expensive, some benchmarks are slow, models vary hugely in size, and you need to handle community models (untrusted code). sandboxing, prioritization queuing, caching partial results, and how you surface results to users were all topics we hit. this round separated candidates in my cohort, i'm told.
round 4: behavioral (45 min) with a principal engineer. mostly about research-to-production tension and how i handle situations where the "right" solution takes 3x as long as the "good enough" solution. they asked about my philosophy on eval metrics, which i found to be a pretty insightful question for a behavioral round.
offer: $205k base + performance bonus, remote US. equity is minimal. worth it for the mission if you're bought in on the open-source ML ecosystem.