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Cuda Machine Learning Performance Engineer Jobs

IMC Inc

Machine Learning Engineer

Dorchester, MA · On-site

$175K - $250K/yr

Machine Learning Engineer Chicago, United States; Hong Kong, Hong Kong; Sydney, Australia As a ... performance environments is a strong plus * Strong engineering skills, including Python, CUDA, or C+

IMC

Machine Learning Engineer

Chicago, IL · On-site

$175K - $250K/yr

As a Machine Learning Engineer, you will play a pivotal role in building systems that drive the ... performance environments is a strong plus * Strong engineering skills, including Python, CUDA, or C+

IMC Inc

Machine Learning Engineer

Chicago, IL · On-site

$175K - $250K/yr

Machine Learning Engineer Chicago, United States; Hong Kong, Hong Kong; Sydney, Australia As a ... performance environments is a strong plus * Strong engineering skills, including Python, CUDA, or C+

Nvidia

OR

... Vision, Machine Learning, or a related field (or equivalent experience) with 12+ years of ... Deep CUDA performance experience, including memory access patterns, shared memory, atomics ...

Nvidia

OR · On-site

$122.40K - $161.30K/yr

Join our dynamic, research-oriented team to help unlock maximum hardware performance for emerging ... Experience with machine learning, especially agentic systems, applied to systems problems. Your ...

Navstar

Machine Learning Engineer

Centreville, VA · On-site

$102K - $144.38K/yr

Familiarity with Nvidia Tools (CUDA, JetPack, TensorRT) and deployment process to Nvidia GPUs ... Is committed to learning from mistakes and driven to improve and enhance performance of oneself ...

Citadel

Machine Learning Engineer

Miami, FL · On-site

Strong engineering skills, including Python, CUDA, C++. * Experience building distributed deep ... machine learning and AI to power our analytics and tackle the market's and our clients' most ...

Motional

Machine Learning Systems Engineer

Boston, MA · On-site +1

We are looking for a Machine Learning Systems Engineer to join our ML Acceleration team. In this ... Design and maintain high-performance GPU kernels in Triton or CUDA for state-of-the-art ML ...

1872 Consulting

Machine Learning Engineer

Chicago, IL · On-site

... using OpenGL or CUDA/CuDNN or TensorRT * General understanding of computer vision concepts ... Hands-on experience applying machine learning and deep learning to vision data, preferably direct ...

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Cuda Machine Learning Performance Engineer information

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How much do cuda machine learning performance engineer jobs pay per year?

As of Jun 3, 2026, the average yearly pay for cuda machine learning performance engineer in the United States is $139,529.00, according to ZipRecruiter salary data. Most workers in this role earn between $140,000.00 and $140,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a CUDA Machine Learning Performance Engineer, and why are they important?

To thrive as a CUDA Machine Learning Performance Engineer, you need strong expertise in parallel programming, GPU architectures, and a solid background in computer science or related fields. Familiarity with CUDA, performance profiling tools (like Nsight), and deep learning frameworks such as TensorFlow or PyTorch is typically required. Analytical thinking, problem-solving, and clear communication are crucial soft skills for diagnosing performance bottlenecks and collaborating with cross-functional teams. These skills ensure optimal machine learning implementations, efficient resource utilization, and advancement of high-performance computing solutions.

What are some common challenges faced by a CUDA Machine Learning Performance Engineer when optimizing ML workloads?

CUDA Machine Learning Performance Engineers often encounter challenges in identifying and resolving performance bottlenecks within GPU-accelerated ML pipelines. Balancing memory usage, maximizing parallelism, and minimizing data transfer between the CPU and GPU are key concerns. Engineers must also keep up with rapid advancements in both hardware and software frameworks, requiring continuous learning and adaptation. Collaboration with data scientists and software engineers is frequent, as you’ll need to translate high-level ML models into efficient, scalable GPU implementations.

What are Cuda Machine Learning Performance Engineers?

Cuda Machine Learning Performance Engineers are specialized professionals who optimize and accelerate machine learning applications using NVIDIA's CUDA platform. They analyze code performance on GPUs, identify bottlenecks, and implement improvements to maximize computational efficiency. Their work often involves collaborating with data scientists and software developers to ensure machine learning algorithms run efficiently on CUDA-enabled hardware. They are proficient in parallel programming, GPU architectures, and performance profiling tools. Their expertise helps organizations achieve faster model training and inference, leading to more effective use of hardware resources.

What is the difference between Cuda Machine Learning Performance Engineer vs Data Scientist?

AspectCuda Machine Learning Performance EngineerData Scientist
Required CredentialsKnowledge of CUDA, GPU programming, machine learning frameworksStatistics, programming, data analysis skills, often a degree in data science or related fields
Work EnvironmentTechnical teams focused on optimizing ML models for GPU hardwareData analysis, model development, business insights
Industry UsageTech, AI, high-performance computing sectorsFinance, healthcare, marketing, tech

The Cuda Machine Learning Performance Engineer specializes in optimizing machine learning models for GPU hardware using CUDA, focusing on performance and efficiency. In contrast, a Data Scientist primarily develops and analyzes models to extract insights from data. While both roles require a strong understanding of machine learning, the Performance Engineer emphasizes technical optimization, whereas the Data Scientist focuses on data analysis and model interpretation.

Machine Learning Engineer

IMC Inc

Dorchester, MA • On-site

$175K - $250K/yr

Other

PTO

This job post has expired today. Applications are no longer accepted.

Job description

Machine Learning Engineer

Chicago, United States; Hong Kong, Hong Kong; Sydney, Australia

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

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.

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