Partnerships Manager (Technical), GTM Team
About the role
About Sygaldry
Sygaldry Technologies is building quantum-accelerated AI servers to exponentially speed up training and inference for AI. By integrating quantum and AI, we're accelerating the path to superintelligence, and addressing the problem of rising compute costs and energy bottlenecks. Sygaldry AI servers combine multiple qubit types within a single, fault-tolerant architecture to deliver the combination of cost, scale, and speed necessary for advanced AI applications. We pioneer new domains in physics, engineering, and AI, tackling the hardest challenges with a grounded, optimistic, and rigorous culture. We're looking for individuals ready to define the intersection of quantum and AI and drive its profound global impact.
We’re hiring a Technical Program Manager, AI to play a leading role at the intersection of GTM, Product, and AI. You’ll be pivotal in bringing our technology to market by translating our product benefit to end-users, while deeply understanding what customers actually need and influencing our product direction.
In this role, you’ll be the connective tissue back into the company from end-users, and work closely with GTM, Product, and AI. If you think like a founder, are excited by autonomy, and love engaging directly with end-users, this role may be a good fit for you.
You’ll play a key role in landing new customer engagements and working closely with partners. You will help define and run the programs that move customer relationships forward, articulating the milestones that progress commercial engagements.
What You’ll Work On
You are excited to be at the forefront of shaping how quantum is used to accelerate AI in practice. You’ll identify targets and be in the room from the very beginning for conversations with AI companies and enterprises, including first meetings, deep-dives, and standing working sessions. While you do not need to be an AI expert or have built and trained large models yourself, you do need to be technically experienced, able to understand AI researcher bottlenecks, speak credibly about model architectures, training and inference workloads, evals, and where our algorithms and hardware benefits end users.