Sr. HPC Engineer - Platforms

Description

The company is building million-qubit, fault-tolerant quantum computers, a goal it has pursued since it was founded in 2016.

Quantum computers use quantum mechanics to tackle problems beyond the reach of today's most powerful supercomputers and AI systems, with uses in energy, pharma, finance, agriculture, transport and materials.

The company's design is built on silicon photonics and uses high-volume chip manufacturing through foundry partners. Photons tolerate heat, resist electromagnetic interference, and work with existing cryogenic cooling and fiber-optic networks.

In 2025 the company raised $1 billion at a $7 billion valuation. In 2026 it signed a $100 million Letter of Intent with the U.S. Department of Commerce under the CHIPS and Science Act, followed by a $125 million agreement with DARPA under Stage C of the Quantum Benchmarking Initiative.

Construction has started on a utility-scale quantum computer in Brisbane, Australia, and the Chicago site continues to expand.

An application, software and industry team develops the algorithms and software that make these machines useful, working with large Fortune 500 companies on quantum solutions.

This role designs and builds HPC software for NVIDIA GPUs and AMD CPUs, using MPI for distributed and parallel computing. You will keep applications efficient as HPC clusters grow to serve expanding global workloads.

You will need a solid base in computational science, parallel algorithms, and GPU and CPU optimization.

Responsibilities

• Write and tune software for NVIDIA GPUs and AMD CPUs to get the most computation out of the hardware.
• Apply MPI, CUDA, OpenMP and other parallel methods so code scales across multi-core CPUs and multi-GPU systems.
• Help design, deploy and grow HPC nodes as data and compute demand rises, and tune software and cluster settings so new nodes join smoothly.
• Choose and implement algorithms suited to GPU or CPU execution for heavy, data-intensive work, keeping performance steady as the node count grows.
• Profile and benchmark applications across scaled environments to find bottlenecks and keep performance consistent across nodes. Use NVIDIA Nsight, AMD μProf and Intel VTune for tuning.
• Work with scientists, engineers and IT staff so applications run well in both research and production.
• Write clear documentation for code and scaling processes, and report performance results and improvements to stakeholders.

Requirements

• Bachelor's or master's degree in computer science, electrical engineering, computational science or a related field.
• At least 3+ years of HPC development focused on parallel, distributed computing and scalable systems, plus 5+ years of scientific software development.
• Proficient in CUDA, with working knowledge of NVIDIA's HPC software stack, including cuDNN, NCCL and TensorRT.
• Expertise in MPI, OpenMP and other parallel programming approaches.
• Strong understanding of scaling clusters and tuning software for distributed, multi-node environments.
• Strong proficiency in C/C++, Python, and Ansible, Terraform and Packer.
• Familiar with profiling and performance tools such as NVIDIA Nsight, AMD μProf and Intel VTune.
• Able to work independently and in a team, solve problems well, communicate clearly and pay close attention to detail.
• A plus: an advanced degree or relevant certifications.
• Preferred: experience with Slurm, PBS or other job scheduling and resource management tools.
• Preferred: knowledge of Singularity or Docker in an HPC context.
• Preferred: understanding of cloud-based HPC scaling or hybrid HPC environments.
• Preferred: experience with GPU-accelerated machine learning frameworks such as TensorFlow, PyTorch or PySCF.

Conditions

The company provides equal employment opportunity to all applicants and employees and does not unlawfully discriminate based on race, religion, sex, gender identity, national origin, citizenship, age, disability, veteran status, sexual orientation or any other legally protected basis.