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D. E. Shaw Research AI/ML Roles Applications are now open! 13 roles spanning LLM research, agentic AI, drug discovery & infrastructure with transparent salary ranges from $115K to $1M base. We're hiring researchers, engineers and scientists for hybrid roles in NYC, Durham or Pittsburgh. Selected candidates get variable comp... show more

Suraj Sharma

14,729 subscribers

122,066 views • 13 days ago •via X (Twitter)

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30 Comments

Internet Labs13 days ago

Seeing a $1M base on a public job posting is wild, but when you're backing roles with custom supercomputing hardware for molecular dynamics, the scale is on another level.

Suraj Sharma13 days ago

The salary catches your attention but the research infrastructure behind these roles is just as wild.

Aryaman13 days ago

People like john jumper work here tho not kids

Suraj Sharma12 days ago

🙌

Tayyab Fraz Chishti11 days ago

For PhD candidates, these roles are a reminder to present research skills as evidence of problem-solving, not just technical keywords.

Suraj Sharma11 days ago

problem-solving ability is the transferable skill behind the research.

Mog13 days ago

Following

Suraj Sharma13 days ago

🙌🤌

SpideyBuilds13 days ago

Can i join ?

Suraj Sharma13 days ago

Just verify the visa sponsorship details in the specific job description.

SpideyBuilds13 days ago

no worries i applied on the link

Suraj Sharma13 days ago

🙌😊

SpideyBuilds13 days ago

I am not gonna get a reply but still

Chandresh Gohel13 days ago

@__acbraingenome you may be good one here.

Suraj Sharma13 days ago

@__acbraingenome apply bro

Sony Thomas13 days ago

Sponsorship available?

Suraj Sharma13 days ago

Looks like it. The roles mention immigration assistance as part of the support offered.

Kashif Ali Khan13 days ago

that 1m base range is wild, definitely checking out the infra roles.

Suraj Sharma13 days ago

The $1M base definitely makes you look twice. Let me know if you end up applying for one of the infra roles!

AI job alerts12 days ago

13 roles across LLM research, agentic AI, and drug discovery, solid range from $115K to $1M. if you're deep in the space, worth a direct look at their postings.

Suraj Sharma12 days ago

The agentic AI roles are especially interesting here.

Yiannis Galdadas11 days ago

From which paper is this simulation? :P

Suraj Sharma11 days ago

If you recognize the simulation, you probably know the paper already 😄

AI Mastery Guide13 days ago

$1M base is wild for these roles

Suraj Sharma13 days ago

The competition for top AI talent is getting wild.

AI Mastery Guide13 days ago

yeah right

Suraj Sharma13 days ago

😊

Big data annotation12 days ago

We are a company focusing on big data processing. We currently have 90 full-time employees and 3,000 part-time employees. We want to see your company's data annotation business.

Suraj Sharma12 days ago

Would be happy to discuss our annotation capabilities and explore potential collaboration.

Big data annotation11 days ago

Hello, we currently have a team of 90 full-time staff with a bachelor's degree or above. I want to accept your company's data annotation business.

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38,090 views • 2 months ago

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11,058 views • 2 months ago

🚨Big Upgrades Are Underway at #NetX!🚨 From a unified ecosystem without obstacles, to an all inclusive #DAO structure, we're entering a new era, one that is transparent, cohesive, and community-driven. ✨A fresh start solving everything with transparency 🔁 Unification of all ecosystem assets 🏛 Pathway to a DAO governance set in motion 💼 Overseas expansion and funding from top-tier institutions #NetX envisions becoming the foundational trust layer for the next generation of Web3 and enterprise applications, built on three core pillars: > AI-powered, Cloud Native Trusted Computing Infrastructure > Deep Data and Enterprise Solutions > Unified Governance with Transparent and Global Participation 🔗Catch up on what’s changing: #NetX isn’t just a platform — it’s a trust layer and a collective movement to reshape the future of #Web3 as a whole. The next wave of enterprise-grade AI and on-chain data infrastructure begins here.🚀 END OF TWEET – Stay vigilant against fake accounts and phishing URLs! #NetX #Update #AI #Agent #Web3 #DAOcommunity #Mainnet

🚨Big Upgrades Are Underway at #NetX!🚨 From a unified ecosystem without obstacles, to an all inclusive #DAO structure, we're entering a new era, one that is transparent, cohesive, and community-driven. ✨A fresh start solving everything with transparency 🔁 Unification of all ecosystem assets 🏛 Pathway to a DAO governance set in motion 💼 Overseas expansion and funding from top-tier institutions #NetX envisions becoming the foundational trust layer for the next generation of Web3 and enterprise applications, built on three core pillars: > AI-powered, Cloud Native Trusted Computing Infrastructure > Deep Data and Enterprise Solutions > Unified Governance with Transparent and Global Participation 🔗Catch up on what’s changing: #NetX isn’t just a platform — it’s a trust layer and a collective movement to reshape the future of #Web3 as a whole. The next wave of enterprise-grade AI and on-chain data infrastructure begins here.🚀 END OF TWEET – Stay vigilant against fake accounts and phishing URLs! #NetX #Update #AI #Agent #Web3 #DAOcommunity #Mainnet

NetX

76,188 views • 1 year ago

4 years ago we were on the brink of AI becoming proprietary and centralized, when OpenAI kept GPT3 closed and VCs started dumping money on researchers. From fully open science, to fully closed, in a matter of months. It was scary, and 1,000+ leading researchers and scientists banded together to show the world that it was possible to do the same work in the open, and build an ecosystem that benefits everyone. That was the BigScience Research Workshop BLOOM project, and it put us back on track to open science, starting with forward-thinking organizations like Meta releasing OPT. Look at us now. Open models have not only caught up, they're state of the art now. Not just LLMs, but models for document AI, speech to text, text to speech, generating images and more. We're closing in on 2 million open weight models on Hugging Face. Thanks for the reminder Thomas Wolf .

4 years ago we were on the brink of AI becoming proprietary and centralized, when OpenAI kept GPT3 closed and VCs started dumping money on researchers. From fully open science, to fully closed, in a matter of months. It was scary, and 1,000+ leading researchers and scientists banded together to show the world that it was possible to do the same work in the open, and build an ecosystem that benefits everyone. That was the BigScience Research Workshop BLOOM project, and it put us back on track to open science, starting with forward-thinking organizations like Meta releasing OPT. Look at us now. Open models have not only caught up, they're state of the art now. Not just LLMs, but models for document AI, speech to text, text to speech, generating images and more. We're closing in on 2 million open weight models on Hugging Face. Thanks for the reminder Thomas Wolf .

Jeff Boudier 🤗

21,947 views • 1 year ago

Shaun Maguire on SpaceX IPO: "Starship is the railroad to space." "In 2019, it was very predictable that Starlink would work and produce a massive amount of revenue, and we called that right." "We're now in a very similar point to where Starlink was in 2019 with AI infrastructure and orbital compute." "I think people are really underestimating how far along the orbital compute product is right now." On the $22 trillion TAM: "What do you think is the TAM for intelligence? What's the TAM for all compute infrastructure 50 years from now?" "People are underappreciating how much infrastructure is going to be built in space and how much easier it is to access energy in space." "We're at the beginning of this AI era, and we're going to double or triple the total amount of intelligence that we have access to over the next decade — so yes, I absolutely believe in the TAM, and I would even argue it's an underestimate." How much is the "railroad to space" worth in the era of AI and orbital compute? Now imagine the railroad company also owns the AI and the orbital compute. Elon Musk Shaun Maguire

Shaun Maguire on SpaceX IPO: "Starship is the railroad to space." "In 2019, it was very predictable that Starlink would work and produce a massive amount of revenue, and we called that right." "We're now in a very similar point to where Starlink was in 2019 with AI infrastructure and orbital compute." "I think people are really underestimating how far along the orbital compute product is right now." On the $22 trillion TAM: "What do you think is the TAM for intelligence? What's the TAM for all compute infrastructure 50 years from now?" "People are underappreciating how much infrastructure is going to be built in space and how much easier it is to access energy in space." "We're at the beginning of this AI era, and we're going to double or triple the total amount of intelligence that we have access to over the next decade — so yes, I absolutely believe in the TAM, and I would even argue it's an underestimate." How much is the "railroad to space" worth in the era of AI and orbital compute? Now imagine the railroad company also owns the AI and the orbital compute. Elon Musk Shaun Maguire

KanekoaTheGreat

39,709 views • 3 months ago

Alex on why AI + fully autonomous labs will compress drug discovery from five years to two: "If scientists are armed with agentic infrastructure that has an intelligence that's a multiple of Einstein, they will do science faster, with higher probability, for lower cost, and have greater impact." "AI coming into the discovery of medicines is one of the biggest unlocks we will see in history because it means we're no longer constrained by our hands or minds. We can identify all n number of hypotheses that could exist, figure out the best experiment to run, and run those 24/7." "The sheer ability to be more comprehensive in our hypothesis set, and to run experimentation at unprecedented scale, means we're going to speed breakthroughs from timelines that take three to five years to timelines that take less than a couple of years."

Alex on why AI + fully autonomous labs will compress drug discovery from five years to two: "If scientists are armed with agentic infrastructure that has an intelligence that's a multiple of Einstein, they will do science faster, with higher probability, for lower cost, and have greater impact." "AI coming into the discovery of medicines is one of the biggest unlocks we will see in history because it means we're no longer constrained by our hands or minds. We can identify all n number of hypotheses that could exist, figure out the best experiment to run, and run those 24/7." "The sheer ability to be more comprehensive in our hypothesis set, and to run experimentation at unprecedented scale, means we're going to speed breakthroughs from timelines that take three to five years to timelines that take less than a couple of years."

Patrick OShaughnessy

39,883 views • 5 months ago

auto-research is starting to gain traction as a very viable paradigm for creating useful research discovery. now, that paradigm is still in its infancy and the infrastructure to hold all that trail of context as the agents blaze through experiments isn't well defined (to say the least). on that topic, I had the chance to chat with my boys francesco and giulio from paradigma about what underlying infra is needed to make this paradigm work. the paradigma's paradigm, which involves copious amount of DAGs, make this auto-research paradigm a paradigmatic case of essential infrastructure. here's the full video in full: - 0:00 - what is missing from auto-research? - 2:02 - giulio and francesco ai journey - 8:10 - research infra is the bottleneck? - 10:18 - paradigma vision of autonomous research - 13:17 - “important discovery per joules” - 17:15 - why is DAG the unit of research for auto-research? - 20:40 - is paradigma trying to replace the research publication? - 24:50 - how does knowledge is shared between experiments in the DAG? - 27:34 - what is even auto-research lol? - 33:53 - the value of the human mind in this auto-research future. - 37:00 - how do you reconcile hallucination in this auto-research paradigm? - 41:33 - the adoption of auto-research across varied fields? - 47:30 - ✨ introduction to the auto-research infrastructure. ✨ - 56:55 - where is the code? - 59:10 - full IDE next? - 1:03:20 - the place of the human in this DAG / code quality? manual node? token spent? - 1:16:02 - who’s the user for auto-research? - 1:18:13 - how to validate bad DAG? - 1:20:18 - ✨ auto-research agent results ✨ - 1:22:53 - ✨ how a big research DAG looks like? ✨ - 1:25:10 - how to get the canonical DAG for the final result? - 1:27:50 - the auto-research DAG being the new pre-print? - 1:30:05 - what’s next for paradigma and the auto-research infra? - 1:35:00 - what are they excited about research wise? enjoyyyyy my guys 🌹

auto-research is starting to gain traction as a very viable paradigm for creating useful research discovery. now, that paradigm is still in its infancy and the infrastructure to hold all that trail of context as the agents blaze through experiments isn't well defined (to say the least). on that topic, I had the chance to chat with my boys francesco and giulio from paradigma about what underlying infra is needed to make this paradigm work. the paradigma's paradigm, which involves copious amount of DAGs, make this auto-research paradigm a paradigmatic case of essential infrastructure. here's the full video in full: - 0:00 - what is missing from auto-research? - 2:02 - giulio and francesco ai journey - 8:10 - research infra is the bottleneck? - 10:18 - paradigma vision of autonomous research - 13:17 - “important discovery per joules” - 17:15 - why is DAG the unit of research for auto-research? - 20:40 - is paradigma trying to replace the research publication? - 24:50 - how does knowledge is shared between experiments in the DAG? - 27:34 - what is even auto-research lol? - 33:53 - the value of the human mind in this auto-research future. - 37:00 - how do you reconcile hallucination in this auto-research paradigm? - 41:33 - the adoption of auto-research across varied fields? - 47:30 - ✨ introduction to the auto-research infrastructure. ✨ - 56:55 - where is the code? - 59:10 - full IDE next? - 1:03:20 - the place of the human in this DAG / code quality? manual node? token spent? - 1:16:02 - who’s the user for auto-research? - 1:18:13 - how to validate bad DAG? - 1:20:18 - ✨ auto-research agent results ✨ - 1:22:53 - ✨ how a big research DAG looks like? ✨ - 1:25:10 - how to get the canonical DAG for the final result? - 1:27:50 - the auto-research DAG being the new pre-print? - 1:30:05 - what’s next for paradigma and the auto-research infra? - 1:35:00 - what are they excited about research wise? enjoyyyyy my guys 🌹

Yacine Mahdid

12,091 views • 3 months ago

Real-time world models represent a fundamental shift in AI. reactor is building the platform for real-time generative video infrastructure, supporting developers who need the tech for use across entertainment, physical AI, and robotics. Co-founders Alberto and Bryce Schmidtchen joined us last week on The Investment Memo, hosted by Partners Bucky Moore and Amber Yang, to talk about the era of world models. The conversation centered around the infrastructure Reactor is building, why real-time models are the edge right now, and current use cases for the product. Alberto and Bryce agreed that world models are shaping the way simulations are created, and that developers need a streamlined platform that can support their ideas. We believe Reactor is positioned to be at the frontier of research into real-time generative models. We look forward to seeing how these models apply across industries. Chapters 00:00 Introduction & Overview of Reactor 01:08 Meet the Hosts & Founders 02:18 The Origin Story: From 3D Assets to World Models 05:07 Real-Time Video Applications Across Industries 06:55 The Open Source World Model Explosion 07:23 Why Infrastructure Is the Opportunity 08:42 Parallels to Past Technology Waves 09:51 Bridging the Research-to-Production Gap 13:13 What Developers Are Building with World Models 16:41 Lessons from Luma AI 18:23 What Apple Vision Pro Taught Bryce About Real-Time Systems 20:48 Company Values & Team Culture 22:40 Series A: What the Capital Unlocks 24:13 Reactor's Five-Year Vision 26:09 Closing Remarks

Real-time world models represent a fundamental shift in AI. reactor is building the platform for real-time generative video infrastructure, supporting developers who need the tech for use across entertainment, physical AI, and robotics. Co-founders Alberto and Bryce Schmidtchen joined us last week on The Investment Memo, hosted by Partners Bucky Moore and Amber Yang, to talk about the era of world models. The conversation centered around the infrastructure Reactor is building, why real-time models are the edge right now, and current use cases for the product. Alberto and Bryce agreed that world models are shaping the way simulations are created, and that developers need a streamlined platform that can support their ideas. We believe Reactor is positioned to be at the frontier of research into real-time generative models. We look forward to seeing how these models apply across industries. Chapters 00:00 Introduction & Overview of Reactor 01:08 Meet the Hosts & Founders 02:18 The Origin Story: From 3D Assets to World Models 05:07 Real-Time Video Applications Across Industries 06:55 The Open Source World Model Explosion 07:23 Why Infrastructure Is the Opportunity 08:42 Parallels to Past Technology Waves 09:51 Bridging the Research-to-Production Gap 13:13 What Developers Are Building with World Models 16:41 Lessons from Luma AI 18:23 What Apple Vision Pro Taught Bryce About Real-Time Systems 20:48 Company Values & Team Culture 22:40 Series A: What the Capital Unlocks 24:13 Reactor's Five-Year Vision 26:09 Closing Remarks

Lightspeed

144,942 views • 3 months ago

The largest fully annotated open egocentric human dataset. Today we're open-sourcing EgoSuite-Open100K with Hugging Face : 100,000 hours of egocentric human data, free for anyone to use. Physical AI has a scaling law of its own now, and its input is human data. That part is settled. What the field still needs is a shared foundation to build on. 15,000+ tasks. 15,000+ real scenes, from factory floors to retail backrooms. Hand pose, body pose and subtask-level semantics, with wrist camera coverage on part of the set. Licensed for commercial training, not research-only. We build the data infrastructure for Physical AI. Open100K is the first public layer of it. The first 10,000 hours are live now, with the rest rolling out in stages.

The largest fully annotated open egocentric human dataset. Today we're open-sourcing EgoSuite-Open100K with Hugging Face : 100,000 hours of egocentric human data, free for anyone to use. Physical AI has a scaling law of its own now, and its input is human data. That part is settled. What the field still needs is a shared foundation to build on. 15,000+ tasks. 15,000+ real scenes, from factory floors to retail backrooms. Hand pose, body pose and subtask-level semantics, with wrist camera coverage on part of the set. Licensed for commercial training, not research-only. We build the data infrastructure for Physical AI. Open100K is the first public layer of it. The first 10,000 hours are live now, with the rest rolling out in stages.

Lightwheel

73,911 views • 28 days ago