Quantum Fault-Tolerance Scientist: Resource & Benchmarking

Amazon; New York (NY); On-site; USD 172,000 - 223,000; Full time

Benefits offered by this job

Health insurance

RSUs

401(k) matching

Paid time off

Job summary

Amazon Braket seeks an Applied Scientist to own resource estimation and workload benchmarking for fault-tolerant quantum workloads on AWS. You will develop models to estimate qubit counts, gate depths, and error budgets, guiding decisions and roadmaps.

You will codify solutions in production-quality code, collaborating with a small team of scientists and engineers and integrating results into the codebase.

Qualifications

• Knowledge of programming languages such as C/C++, Python, Java or Perl.
• Research background in quantum error correction (not limited to resource estimation alone).
• PhD in Physics, Computer Science, Applied Mathematics, or related field with focus on quantum computing.

Responsibilities

• Build and maintain FTQC resource estimation models that determine qubit counts, gate depths, and error budgets for target algorithms.
• Develop benchmarking frameworks that evaluate compilation quality against realistic hardware constraints.
• Produce resource estimates that inform technical decisions and feed into customer readiness work led by the Applications & Engagement team.
• Collaborate with the QEC compilation scientists on how resource estimates feed back into code selection and optimization.
• Connect benchmarking outputs to published materials in coordination with Braket's science and product teams.
• Stay current with the rapidly evolving QEC literature and incorporate new results into estimation models.

Skills

C/C++

Python

Java

Perl

Quantum error correction

Education

PhD in Physics, CS, Applied Math with focus on quantum computing

Tools

Microsoft QRE

Job description

Amazon Braket seeks an Applied Scientist to own resource estimation and workload benchmarking for fault-tolerant quantum workloads on AWS. You will develop models to estimate qubit counts, gate depths, and error budgets, guiding decisions and roadmaps.

You will codify solutions in production-quality code, collaborating with a small team of scientists and engineers and integrating results into the codebase.