About the job
About Us: General Intuition
At General Intuition, we are a pioneering research lab committed to developing foundational models tailored for environments that demand advanced spatial and temporal reasoning. Over the past year, our team has been at the forefront of AI innovation, focusing on agents adept at navigating complex spaces and times, creating world models that serve as training environments, and enhancing video understanding models with a strong emphasis on real-world applicability.
With a successful seed funding round of $133M backed by General Catalyst and Khosla Ventures, we are on a mission to uncover the next generation of intelligence.
Your Role
We are seeking a talented Applied AI Engineer to bridge the gap between our cutting-edge research and the practical applications within our partners' environments, which often face limitations due to hardware, power constraints, and real-world challenges.
You will collaborate with clients in diverse fields such as robotics, simulation, aerospace and defense, manufacturing, logistics, industrial automation, and more. Your primary focus will be on post-training model assessment, data evaluation, and integration to guarantee that our solutions function seamlessly, even within a challenging technical landscape.
Your role will involve working closely with our partners to deeply understand their actual challenges—beyond the specifications outlined in their RFPs. You will analyze their legacy control systems, tackle latency issues, and assess power requirements to determine how our AI can help them achieve unprecedented outcomes.
As a vital part of our feedback loop, you will not only report model failures caused by sensor noise or unexpected physical interactions but also investigate the root causes and collaborate with our team to enhance the underlying architecture. Your efforts will ensure that we develop technology capable of thriving in real-world conditions for years to come.
We are in search of a versatile technical expert. Your journey may have started in systems engineering, physics, or neuroscience before transitioning to machine learning, or vice versa. Proficiency in Python and PyTorch is essential, while familiarity with C++ and low-level hardware constraints is advantageous.
Above all, we value high agency and a passion for being part of an exceptional team.