xAI Machine Learning Engineer Interview Experience — Three Tech Rounds in Two Days, Rejected at the Offer Stage

I got a reach-out this September. I'd heard Elon's company pays really well, so I did a phone screen with the company. It was 15 minutes: we introduced ourselves, then they had me walk through how I collected and processed the data for a project on my resume, followed by some questions about how I think about data vs. downstream task impact. We went a bit deeper into some technical questions from there. Overall the vibe was pretty relaxed, and there was time left at the end for me to ask the interviewer questions. The whole thing lasted about 25 minutes.

About ten minutes after the screen ended I heard I'd passed. I submitted my availability for the next round, and within an hour they had scheduled three technical interviews across the next two days — incredibly fast turnaround.

All three tech interviews were 45 minutes each. I had 2 coding rounds and 1 research round, and I didn't know ahead of time what they'd be testing. (I used GPT to clean up the phrasing of my notes below.)

Coding 1:

You're given a "simulated language model" interface: each call passes in a batch of already-generated token prefixes, and the model returns the corresponding next token (or next tokens) for each sequence. You need to implement a batched sampling/decoding pipeline. The core difficulty is dynamic batching — different sequences finish at different times, so the batch gradually develops empty slots.

End conditions you need to handle:

• hitting the max_tokens limit
• generating a stop token (or stop sequence)

The approach is basically a simplified inference engine:

• Initialization: fill the batch as much as possible (pull requests/sequences from the waiting queue into the running set).
• Iterative decoding: loop, calling the model to generate the next token for every sequence still running in the current batch, and update each sequence's state.
• Sequence completion and reclaiming: when a sequence hits an end condition, mark it done and trigger its callback/return mechanism to deliver the result back to the requester.
• Dynamic refill: after each decoding round, if there are empty slots in the batch, pull new sequences from the waiting queue to fill them; near the end you might not be able to fill the batch completely, so you need to handle a "not-full batch" correctly.

Engineering-wise you generally need to maintain a slot ↔ request/sequence mapping (e.g. slot_id -> sequence_id) so that each position in the batch matches its corresponding request, to avoid results getting mismatched after a dynamic refill. Overall, the process is: sequences keep entering the queue, the scheduler repeatedly calls the model to advance generation, and whenever a slot frees up a new sequence gets pulled in, until every request has been processed.

Coding 2 — GPU node group testing:

There are N nodes. You can call a function test(S) on a set of nodes S:

• if S contains at least one bad node, test(S) = False
• if every node in S is good, test(S) = True

Extra constraints: test can be called in parallel, but the same node can't appear in more than one test running at the same time. Also, |S| >= 2 — you can't test a single node by itself.

Goal: design a function that identifies (or infers the status of) every node as efficiently as possible, discuss the complexity, and ultimately find all the bad nodes.

My idea was simple: find one good node first, then use it to test the others.

After passing the tech rounds I got a 20-minute research talk, and then moved into the offer stage — and that's where I got rejected.

If you're curious about other details, feel free to reply.