
Founding engineer @watneyrobotics
San Francisco, CA
Joined September 2018
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We’ve raised $80 million in Series A funding, co-led by the Valor Atreides AI Fund and Hummingbird Ventures, with continued participation from Conviction, Abstract, A*, and Grant Gordon. This brings our total raised to over $100 million.
We started Watney by asking a simple question -- what can a robot enable in industries where the work remains valuable on an infinite horizon? We found an answer in the fundamental inputs to civilization: energy, matter, and intelligence.
A robot will never be as charming as a barista or as personable as a housekeeper, but it can be more exacting than a surgeon. And, a breakthrough in one quickly becomes the capability of millions. We don’t imitate human motions or processes. We care about the new, differentiated capabilities robots can unlock. We work on problems where our choice of embodiment gives us an order of magnitude advantage, whether through precision, reliability, or scale.
Compute is just beginning to transform society, but it’s constrained by execution. Since 2025, Watney has been serving the largest hyperscalers in the world, helping accelerate their compute rollout through an end-to-end deployment model.
Across hundreds of thousands of hours in customer facilities, our systems have achieved more than four nines of reliability. They’ve enabled our customers to build data centers faster, at larger scales, and with more ambitious architectures. Today, we operate the largest fleet of dexterous robots running 24/7/365 across the United States.
This is all to say, it's an exciting time at Watney. We’re all here because we want to see more ambition in the physical world. We are hiring across all domains. Join us.
I joined watney some time ago. Checkout the only dexterous robots deployed into the real world 24/7
Me and @arbwes are open-sourcing the code to train the low cost robot (<250$) designed by @alexkoch_ai.
This is the policy trained on a sorting task with less than 30 demonstrations.
github.com/Shaka-Labs/ACT
We readapt the ACT implementation by @tonyzzhao that was used to train ALOHA.
The policy was trained on an RTX 3080 in <30min.
This is how data was collected.
How can we empower classical control in robotics with advanced AI *without sacrificing safety and accuracy*?
Introducing NARRATE, a versatile language architecture for optimal control in robotics 🤖
🧵 Paper, prompts and code open-sourced below 👇
In NARRATE we leverage the ability of LLMs to write free-form mathematical expressions such as objective and constraint functions to initialize an optimization-based controller (i.e. MPC) from language instructions.
- Project page: narrate-mpc.github.io/
- Code (Sim): github.com/vateseif/narrate
- Code (ROS): github.com/vateseif/narrate-…
Seif retweeted
I built a web app that generates 3k$ MRR in passive revenue (0 hours a week between me and @ElFrej ).
The lesson? It’s not that difficult.
A thread… 🧵
you should almost always support any startup to succeed.
it’s thanks to others’ success that we all get a chance to try it
Me: You are controlling robots in a kitchen. Move a sponge to the sink but since the sponge is wet find a way to avoid dropping water
GPT:
My father: "I remember being 10 and going to buy fish with my father from a neighboring village. We stocked so much that the motorbike got full and I had to get back home on a boat with the fishermen we bought the fish from. I remember getting seasick from the travel..."
A very simple implementation shows that GPT can already be a useful tool for tuning LQR/PID controllers without the user's need to know anything about them
github.com/vateseif/lmpc-py/…






