High Performance Computing Engineer

About the role

The successful candidate will join a team focused on developing innovative solutions for complex discrete optimization problems, leveraging both classical and quantum computing technologies.

Responsibilities

• Design, implement, and optimize algorithms to solve large-scale discrete optimization problems, including scheduling, logistics, resource allocation, object placement, bin packing and inversion.
• Apply advanced operations research techniques using linear programming, quadratic programming, and combinatorial optimization.
• Develop and deploy heuristic and metaheuristic approaches (e.g., simulated annealing, genetic algorithms, Tabu search) for intractable or non-convex problems.
• Leverage high-performance computing environments to scale and accelerate problem-solving, including multi-core, distributed, and cloud architectures.
• Utilize quantum computing hardware (quantum annealers, gate model devices) and quantum-inspired technologies for problem modeling, algorithm implementation, and performance benchmarking.
• Collaborate with data scientists, software engineers, and domain experts to identify requirements, formulate models, and integrate solutions into operational workflows.
• Conduct performance analysis, benchmarking, and continuous improvement of optimization solvers and pipelines.
• Stay abreast of emerging trends in quantum computing, operations research, and HPC to ensure the use of cutting-edge techniques.

Requirements

• Advanced degree (MS/PhD) in Computer Science, Operations Research, Applied Mathematics, Physics, Engineering, or related fields.
• Proven experience (3+ years preferred) tackling discrete optimization problems at scale, with a portfolio demonstrating end-to-end model implementation and solution.
• Hands-on expertise in linear and quadratic programming, solver technologies (CPLEX, Gurobi, or equivalent).
• Proficiency with heuristic/metaheuristic optimization techniques (simulated annealing, genetic algorithms, Tabu search, etc.).
• Demonstrated experience with quantum computing technologies: quantum annealing (e.g., D-Wave), gate model platforms (e.g., IBM Qiskit, Google Cirq), and/or quantum-inspired optimization solvers.
• Ability to work in high performance/distributed computing environments (HPC clusters, parallel programming, large-scale simulation).
• Proficiency with programming languages such as Python, C++, or Julia, and relevant scientific computing libraries.
• Excellent analytical, problem-solving, and communication skills; able to translate business needs into technical requirements.

Preferred Qualifications

• Experience developing cross-platform optimization packages and APIs.
• Familiarity with hybrid quantum-classical workflows.
• Published research or open-source contributions in optimization or quantum computing fields.
• Experience in domain-specific optimization (supply chain, logistics, manufacturing).