Senior AI Researcher

Vor 3 Tagen

Zürich, Schweiz Jua Vollzeit
What we are looking for

As a Senior AI Researcher, you will develop and train state-of-the-art foundational earth systems models. As part of our fast-moving model research team, you will be working on developing and improving our deep learning-based algorithms and have great creative freedom and influence. Based on the latest publications, you will test and implement state-of-the-art research and, together with the team, build robust and sustainable machine learning pipelines for live operation.

Responsibilities and tasks

  • Researching, implementing, and productionizing machine learning models for renewable power forecasting using satellite imagery, and other multi-modal data.
  • Designing and training models on our GPU cluster with an emphasis on terabyte-scale environmental datasets.
  • Creating novel algorithms to address challenges in climate modeling, from ideation and prototyping to live deployment.
Need to have
  • Expertise in Python with the ability to quickly learn new programming languages as needed.
  • Proficient in machine learning frameworks (e.g., PyTorch, TensorFlow, Keras, FLAX, TFX) and able to stay up to date with emerging tools and frameworks.
  • Strong grasp of machine learning optimization techniques, including gradient clipping, mixed precision training, and layer parallelism
  • Extensive knowledge of transformer architectures (e.g., GPT, Bart, Swin Transformer)
  • Familiarity with advanced techniques such as diffusion models or graph neural networks and their relevance
  • Experience with distributed training and inference architectures, including frameworks like Huggingface Accelerate and Microsoft DeepSpeed.
  • Strong understanding of high-performance computing (HPC) and its role in accelerating machine learning workflows.
  • Proven ability to work with terabyte-scale datasets, particularly those related to climate and geospatial data.
  • General distributed systems knowledge and familiarity with public cloud offerings for ML workflows (e.g., GCP, AWS).
  • Experience with tools like Git, Docker, Xarray, and Zarr.
  • Demonstrated ability to independently solve complex problems with creativity and precision.
  • Collaborative mindset with experience working in interdisciplinary teams.
  • Awareness of the real-world, customer-facing impact of machine learning solutions, particularly in environmental applications.
  • Background in GIS-based data, such as satellite imagery, radar data, or radiosondes.
Nice-to-have
  • Experience with experiment tracking tools (e.g., WandB, MLflow).
  • Hands-on experience running machine learning workloads in production environments.
  • Contributions to open-source projects or the broader climate-focused machine learning community.
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