Senior Engineer - Quantum Error Correction Libraries

Reposted One Month Ago
Be an Early Applicant
6 Locations
Remote or Hybrid
184K-357K Annually
Senior level
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
The Role
Develop and optimize GPU-accelerated algorithms for quantum circuit simulations, collaborating with various teams, and leading technical efforts.
Summary Generated by Built In

NVIDIA's accelerated computing platform has revolutionized HPC and AI, and we have built the cuQuantum SDK to enable quantum computing researchers and framework developers. This role will be part of an engineering team developing, scaling, and optimizing software to accelerate and scale quantum computing and quantum system simulations. Ideal candidates will have experience building simulation software for quantum error correction (QEC) and will have strong affinity for advancing the state-of-the-art in applications of HPC and GPUs to the quantum computing ecosystem. If you are passionate about developing high-performance software and want to help us build libraries to significantly accelerate research and development in this exciting field, we would love to hear from you!

What you will be doing:

  • Developing innovative algorithms to accelerate simulation of QEC workloads at scale.

  • Researching, developing and optimizing GPU-accelerated algorithms across multiple hardware generations

  • Working closely with NVIDIA Research, Developer Technology, and Product Management teams in the areas of quantum computing, HPC technologies, and machine learning

  • Interacting with external partners and researchers to understand their use cases and requirements

  • Providing technical leadership and mentorship to other specialists

What we need to see:

  • Excellent modern C++ and Python programming skills, including, debugging, functional testing, and performance testing.

  • Proven track record to convert mathematical or research-level algorithms into robust, reusable, and well-documented software primitives.

  • Expert understanding of QEC and stabilizer formalism, including binary symplectic representations, stabilizer and destabilizer tableaux, Pauli-frame propagation, mid-circuit measurement, and classical control.

  • Strong understanding of noise models, including Pauli, correlated, and time-dependent noise, as well as exact and approximate representations of non-Pauli noise.

  • Familiarity with the broader quantum software landscape, including Stim, Qiskit Aer, PennyLane, Cirq, cuQuantum SDK, CUDA-Q, or similar development frameworks.

  • Strong communication skills and experience collaborating across research, engineering, and product teams.

  • PhD or MS degree in Computer Science, Applied Mathematics, Physics, Electrical Engineering, or a related scientific or engineering field, or equivalent experience.

  • 8+ years of relevant research or software-development experience.

Ways to stand out from the crowd:

  • Proficiency in near-Clifford or stabilizer simulation techniques.

  • Knowledge of QEC code varieties and procedures, such as surface codes, subsystem codes, color codes, quantum LDPC, lattice surgery, and logical-state preparation. Hands-on experience with QEC decoders.

  • Experience developing performance-critical GPU software using CUDA or a comparable environment.

  • Proficiency in horizontal scaling across multiple nodes or GPUs using NCCL, MPI, NVSHMEM, or equivalent high-performance communication stacks.

  • Aptitude for using agentic or AI-supported development tools alongside steadfast dedication to validation, testing, and code review standards.

#LI-Hybrid

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until August 28, 2026.

This posting is for an existing vacancy. 

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Skills Required

  • Excellent C++ and Python programming skills
  • Experience programming for GPUs
  • PhD or MSc in Computer Science, Applied Math, Physics, or related field
  • 8+ years of experience in relevant field
  • Strong collaboration and communication skills

NVIDIA Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.

  • Equity Value & Accessibility Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
  • Healthcare Strength Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
  • Retirement Support Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.

NVIDIA Insights

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The Company
HQ: Santa Clara, CA
21,960 Employees
Year Founded: 1993

What We Do

NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”

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