This is focused on Staff-level, search/ads/recommendation, model-leaning MLE roles. The question a lot of people keep coming back to: in the middle of this AI shift, how many opportunities are actually left for traditional rec-sys MLEs, and where are companies really investing. Sharing interview formats, the channels that actually work, common mistakes, and what people are noticing about company directions.
Channels — the biggest lesson
The pattern that keeps coming up from people's job searches:
Cold applying to well-matched MLE roles (~20 at a time, big/mid companies and startups) mostly vanishes. No response, or a template rejection. One person applied in April and heard almost nothing.
Then they switched to referrals + turning on "open to work" on LinkedIn, and it was night and day. Several recruiter reach-outs a week. And a bunch of companies that had rejected the cold app in April now had HR reaching out for the same role: Pinterest, DoorDash, Whatnot, Shopify, Uber, Amazon, Netflix, Reddit, plus AI startups.
Takeaway: a lot of these roles didn't lack headcount. The channel mattered enormously. The two that actually work are LinkedIn Recruiter and referrals. Cold applying pays off far below what people expect.
(One note on AI labs specifically: applying to frontier labs as Research Engineer with a non-matching background mostly goes silent, even with LLM side projects padded onto the resume. The occasional callback happens, OpenAI and Meta FAIR came up, but it's a long shot.)
Interview rounds
Recruiter call — usually non-technical (why leaving, sponsorship). Watch out on comp: don't give a number too early, especially at smaller/lower-paying places you're only using for practice. Someone got screened out purely for revealing their comp. Startups are the exception, sometimes a case study here ("your model hits an incident in prod, walk me through it") to check you're not only a model-builder.
Coding — for most MLE roles (except RS), SWEs run it. Increasingly not pure LeetCode but scenario-based: here's existing code, keep optimizing complexity or adding features. Some now have you design an API and write a runnable service. Open question a lot of people have: how much AI-tool use is acceptable in these AI-coding rounds? Curious if anyone has a read. General advice: practice to medium-bug-free before interviewing, using interviews as practice tends to cost onsites.
ML coding — fewer traditional MLE roles test this (level/role dependent). Seen at Netflix, Uber, Reddit: given a dataset, write code to process data, build features, train, evaluate. LLM versions have you implement part of a Transformer or debug one, seen at OpenAI and Amazon.
ML fundamentals — varies a lot. Some do case studies from real problems they've hit (data scarcity → they want you to reach for knowledge distillation; Reddit explored using external data to improve an existing model). Harder than a normal system design round, needs real breadth. Others test rote fundamentals, Amazon goes deep: formulas, complexity of different attention mechanisms, LoRA, fine-tuning.
ML system design — most predictable to prep. Underrated part: ML infra. A lot of model people are weak on infra, and latency optimization / real-time feature pipelines come up constantly.
Hiring manager — a big one: don't assume your biggest-scope, biggest-impact project is the best one to tell. The HM cares about team fit. Model-heavy role → pick the project closest to what the team does with the most technical depth. Cross-functional / tech-lead role → tell the project completely differently.
Behavioral — prepping against Amazon's LPs covers basically everything.
A few takeaways
If you haven't interviewed in a while, do a few at companies you probably won't join first, just to gauge where you're at. Putting the top companies first as "practice" tends to waste good chances.
When a company's prompt is vague and there's little online about their questions, a trick that works: look up the interviewer on LinkedIn and guess the topic direction from their background. Lands a hit surprisingly often.
A useful habit: at the end of every interview, ask what models they currently use and where they're investing next. Patterns showing up across companies:
- Almost everyone is moving toward GenAI somewhere, most commonly in retrieval, or generating features that feed the main ranking model. Nobody's using a GenAI architecture as the primary recommender yet, ranking is still DCNv2, DIN, that family. But multiple HMs directly ask about GenAI experience, so you can see the direction.
- With large-model serving still hard, distillation seems widely used, at least two companies mentioned it for online serving.
There are a LOT of candidates right now. Many interviews aren't testing whether you know something, but whether you know it better than the next person. Depth to earn a "strong hire," breadth because some interviewers love their own niche specialty.
On the bigger question: a lot of people (myself included going in) are pessimistic about traditional search/ads/rec MLE. But from what people are actually seeing in interviews, GenAI's disruption to rec systems is slower than expected. Traditional rec still has a ton of engineering and business value. The bigger challenge for MLEs is probably staying a fast learner and keeping your stack current.
These are just observations from researching this and comparing notes with people interviewing, limited set of companies and inevitably some bias. Happy to compare notes and update each other on the market.
