Who are we?

Join us at the forefront of innovation in the AI sector. Our mission is to accelerate the future of work. We're not merely envisioning the future; we're actively constructing it. Our work is inspired by Ford's assembly line and Ohno's production system.

We are a well-funded Silicon Valley based Series A startup backed by top-tier VCs.

Our founding team boasts a remarkable track record in AI and startup ecosystem, with each member having previously steered AI startups to unicorn status. As we write this new chapter in AI, we invite you to be part of this exciting journey. Be a part of an exceptional team that's not just watching the future unfold but actively making a huge impact in a short amount of time.

We are growing our engineering team centered around our hubs in San Francisco, Toronto and Berlin.

What you will do

• Lead research efforts exploring new neural network foundations and architectures, going beyond incremental improvements
• Advance neural networks broadly, with a particular focus on LLMs as a key application area
• Rethink model representations and computational primitives
• Explore hypercomplex neural networks and alternative mathematical formulations
• Investigate analog, mixed-signal, and custom hardware approaches
• Design and run experiments and prototypes to validate novel hypotheses
• Define evaluation methodologies and compare new approaches against state-of-the-art baselines
• Translate research ideas into scalable implementations and measurable results
• Collaborate closely with engineering to bridge research and practical systems
• Act as the technical lead for this research direction
• Collaborate with the ML engineering team to support complex ML engineering projects by providing cutting-edge insights and guiding key technical decisions

What we look for

• Strong background in machine learning research or advanced ML engineering
• Deep understanding of modern deep learning architectures and their limitations
• Proven ability to formulate original ideas, design rigorous experiments, and iterate based on results
• Strong curiosity about fundamental ML questions, not just applied ML
• Professional experience training or fine-tuning frontier models; extensive hands-on personal projects are also acceptable
• Hands-on experience with reinforcement learning (RL), including areas such as RLHF and policy optimization
• Comfortable working in ambiguous, open-ended research environments while maintaining a strong focus on outcomes, prioritization, and rapid validation of ideas

Strong plus

• Experience optimizing large-scale inference systems (latency, throughput, memory efficiency), including practical understanding of memory movement, KV cache behavior, and quantization
• Experience with hardware-aware ML or hardware design