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Full Time Gpu Engineer Jobs (NOW HIRING)

Machine Learning Engineer

Chicago, IL · On-site

$175K - $250K/yr

Maximize performance in training and inference using GPU hardware and acceleration libraries ... Base salary is only one component of total compensation; all full-time, permanent positions are ...

GPU compiler test engineer Location: San Diego, CA Experience: 4+ Years Duration: Full Time Minimum Qualifications Required: Good hands on experience of Software Releases, Integration and ...

Required : • Strong technical background in GPU/APU programming, C++, and machine learning • ... full-time experience writing efficient, modern C++ and high-level shader code (HLSL SM6, GLSL ...

GPU compiler test engineer Location: San Diego, CA Experience: 4+ Years Duration: Full Time Minimum Qualifications Required: Good hands on experience of Software Releases, Integration and ...

Software Engineer

Santa Clara, CA · On-site

$149.37K - $184.20K/yr

Santa Clara, CA Job Type: Full Time Rate of Pay: The salary range for this position in Santa Clara ... Position requires experience in the following: 1. Programming languages, including C/C++. 2. GPU ...

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Full Time Gpu Engineer information

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How much do full time gpu engineer jobs pay per year?

As of May 31, 2026, the average yearly pay for full time gpu engineer in the United States is $101,752.00, according to ZipRecruiter salary data. Most workers in this role earn between $84,000.00 and $116,500.00 per year, depending on experience, location, and employer.
What cities are hiring for Full Time Gpu Engineer jobs? Cities with the most Full Time Gpu Engineer job openings:
What are the most commonly searched types of Gpu Engineer jobs? The most popular types of Gpu Engineer jobs are:
What states have the most Full Time Gpu Engineer jobs? States with the most job openings for Full Time Gpu Engineer jobs include:

Machine Learning Engineer

IMC

Chicago, IL • On-site

$175K - $250K/yr

Full-time

PTO

Posted 12 days ago


Job description

As a Machine Learning Engineer, you will play a pivotal role in building systems that drive the training and deployment of large-scale ML models across our global operations. You'll collaborate with leading researchers, hardware experts, and software engineers to build robust solutions that maximize the potential of GPU acceleration, distributed computing, and the latest open-source tools. Your work will influence our trading strategies by accelerating experimentation cycles that foster continuous innovation and refinement.
This is a unique opportunity to solve problems at the intersection of advanced machine learning and trading, where your contributions will shape the future of IMC's technology and trading capabilities.
Your Core Responsibilities:
  • Develop large-scale distributed training pipelines to manage datasets and complex models
  • Build and optimize low-latency inference pipelines, ensuring models deliver real-time predictions in production systems
  • Develop libraries to improve the performance of machine learning frameworks
  • Maximize performance in training and inference using GPU hardware and acceleration libraries
  • Design scalable model frameworks capable of handling high-volume trading data and delivering real-time, high-accuracy predictions
  • Collaborate with quantitative researchers to automate ML experiments, hyperparameter tuning, and model retraining
  • Partner with HPC specialists to optimize workflows, improve training speed, and reduce costs
  • Evaluate and roll out third-party tools to enhance model development, training, and inference capabilities
  • Dig into the internals of open-source ML tools to extend their capabilities and improve performance

Your Skills and Experience:
  • 5+ years of experience in machine learning with a focus on training or inference systems
  • Hands-on experience with real-time, low-latency ML pipelines in high-performance environments is a strong plus
  • Strong engineering skills, including Python, CUDA, or C++
  • Knowledge of machine learning frameworks such as PyTorch, TensorFlow, or JAX
  • Proficiency in GPU programming for training and inference acceleration (e.g., CuDNN, TensorRT)
  • Experience with distributed training for scaling ML workloads (e.g., Horovod, NCCL)
  • Exposure to cloud platforms and orchestration tools
  • A track record of contributing to open-source projects in machine learning, data science, or distributed systems is a plus

#LI-DNP
The Base Salary range for the role is included below. Base salary is only one component of total compensation; all full-time, permanent positions are eligible for a discretionary bonus and benefits, including paid leave and insurance. Please visit Benefits - US | IMC Trading for more comprehensive information.
Salary Range
$175,000-$250,000 USD
About Us
IMC is a global trading firm powered by a cutting-edge research environment and a world-class technology backbone. Since 1989, we've been a stabilizing force in financial markets, providing essential liquidity upon which market participants depend. Across our offices in the US, Europe, Asia Pacific, and India, our talented quant researchers, engineers, traders, and business operations professionals are united by our uniquely collaborative, high-performance culture, and our commitment to giving back. From entering dynamic new markets to embracing disruptive technologies, and from developing an innovative research environment to diversifying our trading strategies, we dare to continuously innovate and collaborate to succeed.