Moonshot AI interview questions

From 3 candidate accounts, updated Oct 12, 2026

Candidate accounts: 3
Rounds on average: 1
Rounds in every loop: 1
Open roles at Moonshot AI: 6

How the loop works

This guide draws on 3 first-hand interview accounts from Nowcoder. Candidates describe about 1 round on average, most often technical interviews.

These 3 writeups cover data, software engineering roles. All of them were posted under a forum handle, so we could not verify the authors’ identities.

See open roles at Moonshot AI

Interview experiences at Moonshot AI

AI Infra Algorithm Engineer

Outcome not shared

unknown. 1 round. Shared by 算法打工人小旺 on nowcoder, 2026-10-03

1First roundTechnicalPurely technical round on distributed attention, MoE communication and low-level GPU kernel optimization.Covered: DeepEP normal vs low-latency modes, Ulysses vs Ring Attention, Sharded attention and LSE merging, NVIDIA Ampere/Hopper/Blackwell changes, Software pipelining and occupancy, Persistent kernels, Grouped GEMM scheduling, FP8 quantization granularity, TMA vs cp.async

Agent Developer

Not selected

unknown. 1 round. Shared by AIGC小白入门记 on nowcoder, 1970-01-01

1Technical interviewTechnicalThe interviewer probed agent memory design from many angles, repeatedly following up on summarization details.Covered: ReAct agent project, Short-term memory implementation, Context accumulation across turns, Sliding window plus summarization, Summary trigger thresholds, Incremental vs full re-summarization, Persisting raw history, Long-term memory retrieval timing, Memory injection strategy, Reducing tool schema token cost

What Moonshot AI is hiring for now

6 open roles in our index.

Most-posted roles

Field Marketing Lead1
Head of Marketing1
VP Sales1
Senior Software Engineer1
Staff Software Engineer1
Senior Account Executive1

Top locations

New York City6

Open roles at Moonshot AI

See all 6 roles

Moonshot AI interviews: FAQ

What is the interview process like at Moonshot AI?: This guide draws on 3 first-hand interview accounts from Nowcoder. Candidates describe about 1 round on average, most often technical interviews.
What topics does Moonshot AI test in interviews?: Commonly reported topics include ReAct agent project, Short-term memory implementation, Context accumulation across turns, Sliding window plus summarization, Summary trigger thresholds, Incremental vs full re-summarization.

Round formats

Across all 3 accounts

Technical3

Most asked topics

Context accumulation across turns 2Incremental vs full re-summarization 2Long-term memory retrieval timing 2Memory injection strategy 2Persisting raw history 2ReAct agent project 2Reducing tool schema token cost 2Short-term memory implementation 2

Also tested: ReAct agent project, Short-term memory implementation, Context accumulation across turns, Sliding window plus summarization, Summary trigger thresholds, Incremental vs full re-summarization, Persisting raw history, Long-term memory retrieval timing, Memory injection strategy, Reducing tool schema token cost, DeepEP normal vs low-latency modes, Ulysses vs Ring Attention

These guides summarize public, first-hand interview experiences shared by candidates. They describe the shape of each loop and the topics that came up, not a leaked question bank, and every experience links back to its original source. Processes change often. Treat this as directional prep, not a script.