Logo of Huzzle

AI/ML GPU Software Engineer



23d ago

  • Job
    Mid Level
  • Software Engineering
  • Seattle

AI generated summary

  • You need 3+ years GPU software engineering experience, proficiency in Nvidia frameworks, distributed training skills, Python/Julia knowledge, GPU hardware expertise, container tech experience, problem-solving skills, and a passion for driving innovation in AI/ML models.
  • You will design, optimize, and integrate GPU-accelerated algorithms and software for machine learning models while staying updated on the latest advancements in NVIDIA GPU architectures. Collaborate with diverse teams to enhance deep learning workflows.


  • 3+ years experience in software engineering with a focus on leveraging NVIDIA GPUs for machine learning
  • Proficiency in Nvidia GPU frameworks (CUDA, cuDNN, TensorRT, NCCL) and HPC technologies
  • Understanding of distributed training techniques and frameworks for scaling machine learning workflows across multiple GPUs or nodes
  • Proficiency in programming languages such as Python and Julia, and experience with machine learning packages (e.g., PyTorch, TensorFlow, Jax)
  • Deep expertise in GPU hardware, architecture, and performance optimization
  • Proficiency in GPU profiling and debugging tools such as Nsight and nvprof
  • Skilled in container technologies and orchestration (Docker, Kubernetes) optimized for GPU workflows
  • Excellent problem-solving skills, attention to detail, and a passion for driving innovation in state of the art machine learning models
  • Strong communication and collaboration skills, with the ability to work effectively in cross-functional teams and contribute to a positive team climate
  • Advantageous, but not a must:
  • Experience with stream processing frameworks (e.g., Apache Kafka, Apache Flink) for real-time data ingestion and processing
  • Experience with Google Cloud Platform MLOps tools (e.g., Vertex AI)
  • Knowledge of serverless computing and event-driven architectures for building scalable and cost-effective pipelines
  • Familiarity with monitoring, logging, and debugging tools for distributed systems (e.g., Prometheus, ELK Stack)
  • Experience with applications of AI/ML tools in the energy, weather, or climate fields


  • Design and implement GPU-accelerated algorithms and software frameworks for training and inference of machine learning models.
  • Optimize deep learning workflows for execution on NVIDIA GPUs using frameworks such as TensorFlow, PyTorch, or MXNet.
  • Develop custom CUDA kernels and GPU-accelerated libraries to accelerate performance-critical operations in machine learning pipelines.
  • Collaborate with data scientists, machine learning engineers, and software developers to integrate GPU-accelerated components into end-to-end machine learning systems
  • Stay up to date with the latest advancements in NVIDIA GPU architectures, deep learning frameworks, and GPU-accelerated libraries
  • Contribute to a team culture where diverse viewpoints, backgrounds and expertise are welcomed


What experience is required for the role of AI/ML GPU Software Engineer?

The ideal candidate should have at least 3+ years of experience in software engineering with a focus on leveraging NVIDIA GPUs for machine learning. They should also have proficiency in Nvidia GPU frameworks, understanding of distributed training techniques, and expertise in GPU hardware and performance optimization.

What are the key responsibilities of an AI/ML GPU Software Engineer?

The responsibilities include designing and implementing GPU-accelerated algorithms and software frameworks, optimizing deep learning workflows for NVIDIA GPUs, developing custom CUDA kernels, collaborating with data scientists and engineers, staying up to date with advancements in GPU architectures, and contributing to a team culture of diversity and innovation.

What programming languages and frameworks should an AI/ML GPU Software Engineer be proficient in?

The candidate should have proficiency in programming languages such as Python and Julia, as well as experience with machine learning packages like PyTorch, TensorFlow, and Jax. They should also be familiar with Nvidia GPU frameworks like CUDA, cuDNN, TensorRT, and NCCL.

What additional skills would be advantageous for an AI/ML GPU Software Engineer?

Experience with stream processing frameworks, Google Cloud Platform MLOps tools, serverless computing, event-driven architectures, monitoring tools for distributed systems, and applications of AI/ML in energy, weather, or climate fields would be advantageous for this role.

Our energy for a sustainable life.


Mission & Purpose

RWE is leading the way to a green energy world. With an extensive investment and growth strategy, the company will expand its powerful, green generation capacity to 50 gigawatts internationally by 2030. RWE is investing €50 billion gross for this purpose in this decade. The portfolio is based on offshore and onshore wind, solar, hydrogen, batteries, biomass and gas. RWE Supply & Trading provides tailored energy solutions for large customers. RWE has locations in the attractive markets of Europe, North America and the Asia-Pacific region. The company is responsibly phasing out nuclear energy and coal. Government-mandated phaseout roadmaps have been defined for both of these energy sources. RWE employs around 19,000 people worldwide and has a clear target: to get to net zero by 2040. On its way there, the company has set itself ambitious targets for all activities that cause greenhouse gas emissions. The Science Based Targets initiative has confirmed that these emission reduction targets are in line with the Paris Agreement. Very much in the spirit of the company’s purpose: Our energy for a sustainable life.