science at deepmind, prev: google brain
Joined November 2017
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Simon Batzner retweeted
25 Oct 2025
Very excited about our progress on materials! Super cool work, come join the AI for Science team.
23 Oct 2025
Our team at DeepMind is growing (again). 🚀
We're tackling grand challenges in semiconductors, magnets, energy materials, superconductors, and beyond.
Join us! Two positions below.
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Simon Batzner retweeted
May 29
Apply to work on hard core science and research
May 28
Our team at DeepMind is growing.
We've assembled a world class physics material science team and are building an experimental lab.
If you want to solve real problems at the intersection of AI material science to unlock a technological revolution - this is the place. Apply 👇
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Simon Batzner retweeted
May 28
Our team at DeepMind is growing.
We've assembled a world class physics material science team and are building an experimental lab.
If you want to solve real problems at the intersection of AI material science to unlock a technological revolution - this is the place. Apply 👇
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May 28
real ones remember
May 28
"internal covariate shift"
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Simon Batzner retweeted
May 16
You can believe the inner quote while realizing how wrong the outer quote is.
3-4 years ago people were amazed LLMs could answer simple questions. Now they’re complaining the critical infra they write has too many bugs. 3-4 years from now the models will be unrecognizably good.
in 3-4 years companies will be hiring INSANELY expensive consultants to unscrew their Mythos-created spaghetti critical infrastructure, which was 99.9% autonomous, until the 0.1% catastrophy hit
don't underestimate humans. We are amazing!
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Personal news: I‘ve been promoted to Staff Research Scientist at DeepMind. The past year has been a crazy ride pushing the frontier of physics and AI with the team.
Super excited about where we’re headed. 🧑🍳
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Jan 30
Our team at @GoogleDeepMind is hiring for multiple roles in AI materials. 🚀
We‘re pursuing an ambitious research program in materials and are looking for the world‘s best
- semiconductor experts
- ml materials researchers
- lab automation engineers
Vibes are great, join us!
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Jan 30
Semiconductor role: job-boards.greenhouse.io/dee…
Senior lab automation engineer: job-boards.greenhouse.io/dee…
ML materials RS: job-boards.greenhouse.io/dee…
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More about our work here:
25 Oct 2025
Very excited about our progress on materials! Super cool work, come join the AI for Science team.
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1 Nov 2025
Yet another NequIP in the top 10 with EquFlash, this time with some very clever accelerations! Bringing the total to ….? I’ll leave it to you how to count :)
One question this raises is what a lot of folks have told me recently both on here and in private: they find it “disheartening” (to quote @SamMBlau) that we’ve had the same sota architecture since January 2021 now.
My answer is always the same: we’re not building these models for the sake of building models. We’re building them because there are fundamental challenges that require the discovery of novel materials. These algorithms accelerate that. If the FF architecture isn’t the bottleneck, you should stop optimizing it and focus on more interesting problems (data, data, data, evals, scalability, and above all, actually finding and making materials).
I can think of at least one other field that flourished when they stopped playing the architecture game.
Take my words with a grain of salt though. I was told at APS 2019 by a very “senior” person in the field that the fitting problem of MLIPs was “solved”. That turned out it be horribly wrong. I’m rooting for every grad student to make a meaningful dent in this problem. And who knows, maybe there is more juice to be squeezed beyond a 1mev/atom MAE difference.
(also if you’re building molecular FFs, different story, this is a materials benchmark)
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1 Nov 2025
And obviously papers like this that accelerate the methods meaningfully are incredibly useful and a step in the right direction.
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1 Nov 2025
Our team at GDM is hiring a software engineer to help us build the future of AI-driven scientific discovery. 🚀
If you want to work with us on scaling scientific simulations, AI agents, and our latest training runs, come join us!
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1 Nov 2025
Position is based in Mountain View, CA.
Note that this is separate from the postings from last week.
job-boards.greenhouse.io/dee…
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1 Nov 2025
This team is the most talent-dense group of ppl I’ve ever worked with. Join the fun!
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23 Oct 2025
Our team at DeepMind is growing (again). 🚀
We're tackling grand challenges in semiconductors, magnets, energy materials, superconductors, and beyond.
Join us! Two positions below.
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23 Oct 2025
Both London-based 🇬🇧
Post 1: job-boards.greenhouse.io/dee…
Post 2: job-boards.greenhouse.io/dee…
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23 Oct 2025
More from @brendanbc2:
x.com/brendanbc2/status/1981…
23 Oct 2025
At @GoogleDeepMind, our world-class team of quantum materials experts, engineers, and AI researchers is using massive-scale compute and AI to revolutionize materials discovery.
We're expanding! We are looking for truly exceptional computational materials scientists. 👇
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