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Research Computing Jobs in Kansas (NOW HIRING)

EnCharge AI

EnCharge's robust and scalable next-generation in-memory computing technology provides orders-of ... Research and develop quantization-aware training (QAT) and post-training quantization (PTQ ...

University of Kansas

Research Engineer Senior

Lawrence, KS

$98.50K - $135.30K/yr

The mission of the IDL is to design and develop custom instruments and laboratory computing systems for research groups in the natural sciences and engineering and has supported the KU Research ...

Waabi

$158K - $269K/yr

... computing techniques for efficient computation. - Publications in top-tier conferences or journals related to high-performance computing, image processing, computer graphics, computer vision, machine ...

Genesis Health Clubs

Accountant

Wichita, KS

Researching and analyzing accounting data to develop specialized reports for management * Audit ... Computing and paying retail sales tax * Preparation of tax returns * Payroll taxes

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How much do research computing jobs pay per hour?

As of May 30, 2026, the average hourly pay for research computing in Kansas is $19.82, according to ZipRecruiter salary data. Most workers in this role earn between $15.43 and $21.20 per hour, depending on experience, location, and employer.

What is a Research Computing job?

A Research Computing job involves supporting computational and data-intensive research by providing expertise in high-performance computing (HPC), data management, software development, and cloud technologies. Professionals in this field work closely with researchers to optimize code, manage large datasets, and ensure efficient use of computing resources. They may also develop and maintain computing infrastructure, troubleshoot technical issues, and assist with grant proposals requiring specialized computing capabilities.

What are the key skills and qualifications needed to thrive in the Research Computing position, and why are they important?

To excel in Research Computing, a strong background in computer science, data analysis, and scientific research methodologies is essential, often supported by an advanced degree in a STEM field. Familiarity with high-performance computing (HPC) systems, cluster management tools, programming languages (such as Python, R, or C++), and certifications in cloud platforms or data science are commonly required. Effective problem-solving, collaboration, and communication skills help professionals work closely with researchers and interdisciplinary teams. These skills and qualities are crucial for supporting complex computational research, ensuring efficient system use, and driving innovation in scientific discovery.

What are the typical day-to-day responsibilities of someone working in Research Computing?

In a Research Computing role, you will often support researchers by managing and optimizing access to high-performance computing resources, troubleshooting technical issues, and assisting with the setup of specialized software or tools. You may also help develop computational workflows, maintain data storage systems, and guide users on best practices for maximizing resource efficiency. Collaboration with scientists, faculty, and IT staff is common, as you play a key role in enabling advanced research projects. This hands-on, problem-solving environment provides opportunities to learn new technologies and contribute directly to impactful scientific work.
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Research Computing jobs near you
Infographic showing various Research Computing job openings in Kansas as of May 2026, with employment types broken down into 54% Full Time, 43% Part Time, and 3% Contract. Highlights an 98% Physical, and 2% Remote job distribution, with an average salary of $41,223 per year, or $19.8 per hour.

AI Research Engineer

EnCharge AI

Other

Posted 19 days ago

Job description

EnCharge AI is a leader in advanced AI hardware and software systems for edge-to-cloud computing. EnCharge's robust and scalable next-generation in-memory computing technology provides orders-of-magnitude higher compute efficiency and density compared to today's best-in-class solutions. The high-performance architecture is coupled with seamless software integration and will enable the immense potential of AI to be accessible in power, energy, and space constrained applications. EnCharge AI launched in 2022 and is led by veteran technologists with backgrounds in semiconductor design and AI systems.

About the Role

EnCharge AI is looking for an experienced AI Research Engineer to optimize deep learning models for deployment on edge AI platforms. You will work on model compression, quantization strategies, and efficient inference techniques to improve the performance of AI workloads. 

Responsibilities

  • Research and develop quantization-aware training (QAT) and post-training quantization (PTQ) techniques for deep learning models.

  • Implement low-bit precision optimizations (e.g., INT8, BF16).

  • Design and optimize efficient inference algorithms for AI workloads, focusing on latency, memory footprint, and power efficiency.

  • Work with frameworks such as PyTorch, ONNX Runtime, and TVM to deploy optimized models.

  • Analyze accuracy trade-offs and develop calibration techniques to mitigate precision loss in quantized models.

  • Collaborate with hardware engineers to optimize model execution for edge devices, and NPUs.

  • Contribute to research on knowledge distillation, sparsity, pruning, and model compression techniques.

  • Benchmark performance across different hardware and software stacks.

  • Stay updated with the latest advancements in AI efficiency, model compression, and hardware acceleration. 

Qualifications

  • Master's or Ph.D. in Computer Science, Electrical Engineering, or a related field.

  • Strong expertise in deep learning, model optimization, and numerical precision analysis.

  • Hands-on experience with model quantization techniques (QAT, PTQ, mixed precision).

  • Proficiency in Python, C++, CUDA, or OpenCL for performance optimization.

  • Experience with AI frameworks: PyTorch, TensorFlow, ONNX Runtime, TVM, TensorRT, or OpenVINO.

  • Understanding of low-level hardware acceleration (e.g., SIMD, AVX, Tensor Cores, VNNI).

  • Familiarity with compiler optimizations for ML workloads (e.g., XLA, MLIR, LLVM). 

EnchargeAI is an equal employment opportunity employer in the United States.

Apply