About the job
At Orbital Industries, we harness the power of artificial intelligence to revolutionize data center hardware, creating solutions that significantly surpass industry standards. Our innovative AI technology conducts atomic-level simulations of materials and evaluates millions of hardware configurations, identifying optimal designs that set new benchmarks in performance.
Every deployment enhances our AI's capabilities, establishing a self-reinforcing cycle where improved models lead to superior hardware, ultimately enabling even more advanced AI solutions. We are not just participants in the AI revolution; we actively drive its momentum.
While our primary focus is on data centers, which are characterized by urgent market demands and stringent specifications, the AI-driven development process we've perfected is applicable across any intricate physical system. Data centers serve as our initial proving ground, showcasing the vast potential of our technology.
With operations in London, Canada, and the USA, we are building diverse teams in machine learning research, product development, mechanical engineering, and chemical engineering. If you're eager to explore the intersection of AI and material science, we want to hear from you.
As a Staff Machine Learning Engineer, you will design and implement state-of-the-art AI systems for the multi-scale design of physical technologies. This role emphasizes the integration of world-class models that simulate both atomic motion and fluid dynamics in extensive data centers. You will collaborate closely with our scientists and engineers to co-design across these scales, leveraging advanced domain agents.
In this position, you will establish high technical standards and guide projects from initial prototype stages to full production deployment. We seek a candidate who is passionate about craftsmanship, values continuous learning, and is dedicated to developing scalable systems. A humble approach and a genuine enthusiasm for applying AI to tackle significant global industrial challenges are essential.
Key Responsibilities
Establish and uphold high technical standards: Ensure excellence in code quality, system architecture, and machine learning practices through hands-on coding and technical evaluations.
Design robust systems: Create well-engineered solutions that are scalable and maintain a balance between research pace and production needs.
Drive technical decision-making: Lead decisions on model selection, training methodologies, and deployment strategies.
