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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 görüntüleme • 3 ay önce •via X (Twitter)

5 Yorum

Russian Bot profil fotoğrafı
Russian Bot3 ay önce

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 profil fotoğrafı
artyst3 ay önce

interesting, let's see how it unfolds

David Song profil fotoğrafı
David Song3 ay önce

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 🌖 profil fotoğrafı
Eclipse 🌖3 ay önce

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 profil fotoğrafı
GrillePainVert3 ay önce

Basically they still need 10 human software developers.

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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 görüntüleme • 9 ay önce