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High Performance Computing Research Scientist Jobs

SLB

High Performance Computing Engineer

Sunnyvale, CA · On-site

Advanced degree (MS/PhD) in Computer Science, Operations Research, Applied Mathematics, Physics ... Ability to work in high performance/distributed computing environments (HPC clusters, parallel ...

VTG

High-Performance Computing Engineer

Mclean, VA · On-site

Overview iota IT, a subsidiary of VTG, is seeking a High-Performance Computing Engineer in McLean ... Bachelor's Degree in Computer Science, Engineering or related field. * Experience with NVidia, Cray ...

VTG

High-Performance Computing Engineer

Mclean, VA · On-site

Overview iota IT, a subsidiary of VTG, is seeking a High-Performance Computing Engineer in McLean ... Bachelor's Degree in Computer Science, Engineering or related field. * Experience with NVidia, Cray ...

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High Performance Computing Research Scientist information

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How much do high performance computing research scientist jobs pay per year?

As of Sep 8, 2026, the average yearly pay for high performance computing research scientist in the United States is $130,117.00, according to ZipRecruiter salary data. Most workers in this role earn between $107,500.00 and $173,000.00 per year, depending on experience, location, and employer.

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For High Performance Computing Research Scientist jobs, the most frequently searched job titles are:

Infographic showing various High Performance Computing Research Scientist job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 16% Part Time, and 3% Contract. Highlights an 90% Physical, 1% Hybrid, and 9% Remote job distribution, with an average salary of $130,117 per year, or $62.6 per hour.

High Performance Computing Engineer

Onesubsea

Sunnyvale, CA • On-site

$125 - $150/hr

Other

Posted 6 days ago

Job description

We are seeking a highly skilled High Performance Computing (HPC) Engineer with a strong background in modeling and solving complex discrete optimization problems. The ideal candidate will demonstrate deep expertise in operations research, advanced mathematical programming, and quantum computing technologies. Your solutions will address challenges such as scheduling, logistics, resource allocation, object placement, bin packing and inversion for large, data-driven systems demanding exceptional performance and precision.

Key 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.
Required Qualifications
  • 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).
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