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Google DeepMind pre-training lead explains two skills with massive demand by AI frontier labs: > Kernel Development > Low Level Performance Engineering

81,607 次观看 • 3 个月前 •via X (Twitter)

5 条评论

Russian Bot 的头像
Russian Bot3 个月前

If I was running Google I would forbid any “Googler” from doing interviews until Googles position in the AI race was no longer a complete joke.

artyst 的头像
artyst3 个月前

interesting, let's see how it unfolds

David Song 的头像
David Song3 个月前

sw mentioned in the same sentence as low latency feels like a crime hft mastered latency years ago and it's not via writing more layers of sw

Eclipse 🌖 的头像
Eclipse 🌖3 个月前

Makes sense—demand for kernel dev and low-level perf engineering has been climbing with every new training run. Curious how many of those roles are going to ex-FAANG systems folks vs. fresh grads.

GrillePainVert 的头像
GrillePainVert3 个月前

Basically they still need 10 human software developers.

相关视频

Gemini 3, scaling laws and the 'finite data' era: my conversation with Sebastian Borgeaud, research engineer at Google DeepMind and a pre-training lead for Gemini 3 00:00 – Cold intro: “We’re ahead of schedule” + AI is now a system 00:58 – Oriol Vinyals's “secret recipe”: better pre- + post-training 02:09 – Why AI progress still isn’t slowing down 03:04 – Are models actually getting smarter? 04:36 – Two–three years out: what changes first? 06:34 – AI doing AI research: faster, not automated 07:45 – Frontier labs: same playbook or different bets? 10:19 – Post-transformers: will a disruption happen? 10:51 – DeepMind’s advantage: research × engineering × infra 12:26 – What a Gemini 3 pre-training lead actually does 13:59 – From Europe to Cambridge to DeepMind 18:06 – Why he left RL for real-world data 20:05 – From Gopher to Chinchilla to RETRO (and why it matters) 20:28 – “Research taste”: integrate or slow everyone down 23:00 – Fixes vs moonshots: how they balance the pipeline 24:37 – Research vs product pressure (and org structure) 26:24 – Gemini 3 under the hood: MoE in plain English 28:30 – Native multimodality: the hidden costs 30:03 – Scaling laws aren’t dead (but scale isn’t everything) 33:07 – Synthetic data: powerful, dangerous? 35:00 – Reasoning traces: what he can’t say (and why) 37:18 – Long context + attention: what’s next 38:40 – Retrieval vs RAG vs long context 41:49 – The real boss fight: evals (and contamination) 42:28 – Alignment: pre-training vs post-training 43:32 – Deep Think + agents + “vibe coding” 46:34 – Continual learning: updating models over time 49:35 – Advice for researchers + founders 53:35 – “No end in sight” for progress + closing

Matt Turck

51,484 次观看 • 9 个月前