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

We are building our core AI Weather Lab team to explore creative applications of AI methods to challenges in the renewable energy industry To lead our efforts in developing industry-leading AI models, we are seeking an experienced and visionary Senior Research Engineer with deep expertise in leveraging NVIDIA GPUs for advanced machine learning applications.

As a senior member of our engineering team, you will be responsible for architecting, developing, and optimizing GPU-accelerated software solutions that enable the training, inference, and deployment of machine learning models at scale. You will play a pivotal role in shaping and maintaining the tech stack of the lab and will collaborate closely with senior scientists and engineers from diverse fields. At RWE, the third-largest producer of wind power globally, your contributions will significantly impact renewable energy production worldwide.

• Architect 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.
• Lead collaborations with data scientists, machine learning engineers, and software developers to integrate GPU-accelerated components into end-to-end machine learning systems.
• Stay abreast of the latest advancements in NVIDIA GPU architectures, deep learning frameworks, and GPU-accelerated libraries.
• Foster a team culture that values diverse viewpoints, backgrounds, and expertise.

• 7+ years of software engineering experience with a focus on leveraging NVIDIA GPUs for machine learning.
• Mastery of Nvidia GPU frameworks (CUDA, cuDNN, TensorRT, NCCL) and HPC technologies.
• In-depth 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.
• Advanced proficiency in GPU profiling and debugging tools such as Nsight and nvprof.
• Skilled in container technologies and orchestration (Docker, Kubernetes) optimized for GPU workflows.
• Exceptional problem-solving skills, meticulous attention to detail, and a passion for driving innovation in cutting-edge machine learning models.
• Strong leadership, communication, and collaboration skills, with the ability to work effectively in cross-functional teams and contribute to a positive team climate.
• 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.

• 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).
• Experience with applications of AI/ML tools in the energy, weather, or climate fields.

• Dynamic of a start-up in a well established environment
• Using cutting edge technology to solve challenging matters
• Contribution to the company goals of energy transition
• Hybrid working model with 3 days in the office to foster collaboration and teamwork
• Diverse and multicultural team in a highly dynamic and rapidly growing business
• Salary range $150,000 - $220,000