RWE Supply & Trading Americas, LLC
To start as soon as possible, fulltime, permanent

Your future plans

We are building our core AI Lab team to explore creative applications of AI methods to challenges in the renewable energy industry. To help us innovate and build industry-leading AI models, we are seeking a highly motivated software engineer with expertise in leveraging NVIDIA GPUs for machine learning applications.

As a key member of our engineering team, you will be responsible for designing, developing, and optimizing GPU-accelerated software solutions that enable the training, inference, and deployment of machine learning models at scale. The successful candidate will play a critical role in developing and maintaining the tech stack of the lab and will work with senior scientists and engineers from a variety of fields. RWE is the third largest producer of wind power in the world, so you will have a material impact on renewable energy production across the globe. We operate a hybrid model that requires employees to be in the office at least three days per week to foster collaboration and teamwork, while offering the flexibility to work remotely as applicable.

• 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

• 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

• 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

• Task oriented and hybrid working model
• Diverse and multicultural team in a highly dynamic and rapidly growing business
• Relocation expenses for moving to the greater Seattle area if you are not already local
• Salary: $120,000 - $170,000