Performance Optimization Team Lead

Requirements

• 3+ years of experience in managing a research or engineering team delivering tools in a R&D environment
• Proven track record of leading a team that delivers on complex projects on time
• Strong organizational and time management skills
• Exceptional written and verbal communication skills in English
• Master’s degree in Machine Learning, Optimization, Quantum Computing or related fields (Computer Science, Applied Mathematics, Theoretical Physics)

What the job involves

• The Platform department builds the software and computational infrastructure that powers Alice & Bob’s quantum computers
• Our scope spans scientific software, cloud/GPU compute, simulation tools, and the orchestration systems required to design, calibrate, and operate our hardware
• The Performance Optimization team applies machine learning and optimization tools across the entire quantum chip lifecycle, from design to error correction
• As Team Lead, you will challenge traditional methods to deliver scalable, production-ready calibration and control software
• Your Mission: Build and lead an interdisciplinary team of engineers, theorists, and ML experts to identify bottlenecks in our quantum control stack
• You will prototype novel solutions, transform them into fast, reliable production software, and integrate them directly into the critical path of our fault‑tolerant operations
• Define the Performance Optimization team strategy by taking inputs from experts, leadership and other teams
• Oversee team projects, ensuring objectives match the company ambitions, integrate with the stacks of other teams, and are delivered on time in production
• Manage team resources and priorities, ensuring alignment with department goals
• Hire talented and complementary individuals to grow the team, following the department hiring plan
• Foster a supportive environment for team development and career growth
• Manage external research collaborations
• Represent the company at conferences and workshops focused on ML for quantum