Member of Technical Staff; ML Research

Join us at the frontier of AI, acquisitions, and transformation. Our mission is to accelerate the future of work. Our work is inspired by Ford's assembly line and Ohno's production system.

We are a well-funded company pioneering a new model of acquisition-led growth. Instead of building software companies the traditional way, chasing customers with sales and marketing, we acquire services businesses and reinvent them from the inside out.

Our founding team boasts a remarkable track record in AI and company-building, with each member having previously steered AI startups to unicorn status (e.g. Cresta.ai).

Our approach is not SaaS:
We drive growth through acquisition and technology is our transformation engine. Each company we bring on board comes with complex workflows, legacy systems, and antiquated data structures. We turn that complexity into opportunity, designing platforms that unlock efficiency, scale, and profitability.

• 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 hyper complex 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.

• 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, including areas such as RLHF and policy optimization.
• Comfort 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, including latency, throughput, memory efficiency, KV cache behavior, and quantization.
• Strong plus: experience with hardware-aware ML or hardware design.

Apply

Choose the application path for your location.