cofounder @ProximalHQ
San Francisco, CA
Joined March 2021
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If I had to name one thing that we’ve done right at Proximal, it would be hiring.
Outside of raw technical talent, I’ve never seen a place at which people care as much and have as much fun together (I’ve been told that coming to the office feels like going back to college)
Proximal builds infrastructure that enables AI to improve from real-world experience.
In the past year, we have grown to more than $200M in annualized revenue helping frontier labs and enterprises improve coding agents. Now, we are expanding beyond software engineering.
Recent progress in mathematics demonstrates what happens when models are trained in domains with massive amounts of public data and easy verifiability, making it easy to find weaknesses, generate training tasks, and iterate quickly.
We build infrastructure that enables this feedback loop in other domains: we are excited about a world in which AI systems design targeted drugs, accelerate chip design, and rewrite legacy software that still runs hospitals, power grids, governments, and other critical infrastructure.
Since starting, we have assembled a small team of researchers and engineers and built world-class infrastructure for post-training and synthetic data research to overcome the challenges of scaling manual data curation.
Our team comes from Cursor, Google DeepMind, Meta Superintelligence, Prime Intellect, Citadel, and Jane Street. More than half are former founders.
We’re fortunate to be backed by investors who share that vision, including @generalcatalyst who led our $15M Seed Round at a $300M valuation, as well as @chemistry, @svangel, @dvlamoen and individuals like @LiamFedus, @kevinweil, and @bernhardsson
Congrats! Aryaman and I met as undergrads at NAACL 2022, so I've been lucky enough to have a front-row seat to his PhD journey and watch him become a leading interpretability researcher.
Outside of this, he is a great person. I'm sure he will do extremely well at Anthropic!
I defended my Ph.D. @stanfordnlp a few months ago, and recently started @AnthropicAI on the Interpretability team!
(pictured: my awesome defence committee members)
I love how broad the space of possible valid solution strategies is for this task. It provides a great way to measure technical decision making in models!
We are hiring for the following roles (DMs open!):
1) Research Engineer: Run eval and training experiments to understand how to improve model capabilities
2) Platform Engineer: Build infrastructure to orchestrate millions of agents and sandboxes per day
3) Technical Generalist: Work closely with our enterprise and frontier lab customers and own projects end to end
Justus Mattern retweeted
During rollouts, sandboxes spend most of their time idle.
To improve bin packing, we aggressively overcommit resources and prioritize scheduling sandboxes on more full nodes. To optimize latency, we schedule sandboxes in an image cache-aware fashion and compress images with zstd (instead of gzip).
Justus Mattern retweeted
Generational technology if we want it to be.
“Owning your intelligence is owning your future.”
@latkins, CTO of @arcee_ai, on open weights and everything built on top of them.
Open Intelligence Summit keynote streams live Oct 13: do.co/4zo4nZS
Justus Mattern retweeted
highly recommend folks to take a peek here some very interesting alpha is being thrown at your screen
Justus Mattern retweeted
PSA to data cos: you need to build frontier-level training infra if you want to prove the value of your training data
the data game is past its slop era
Justus Mattern retweeted
Today, we are sharing more about our post-training infrastructure @ProximalHQ
We build on top of @modal GPUs and Kubernetes to support our experiments. Had a ton of fun working on this, and we tried to make the blog as educational as possible!
More cool details below :)
This attitude is reflected in our hiring - we have not blitzscaled, but instead taken the time needed to convince the best engineers and researchers we know to join the team.
If you want to work in an extremely talent-dense team, please reach out
To make agents competent in the real world, we increasingly care about design choices, decision making and other skills that are not strictly "verifiable".
I cannot trust my agent to autonomously handle open-ended, long-horizon tasks like "please reduce our infra costs" without making decisions that will come to bite us later
Capturing the nuances required to improve these skills, and training on increasingly longer-horizon tasks in which we'd like to rely on more dense feedback requires non-conventional choices w.r.t reward design.
To validate and experiment with these design choices, training is absolutely crucial. Companies that work on data should look more like research labs than what most of them look like today.