8 of 8 Remote Dask Jobs in London

in a data-driven role, with exposure to: scaling and optimising ML models, training and serving foundation models at scale (federated learning a bonus); distributed computing frameworks (e.g., Spark, Dask) and high-performance computing frameworks (MPI, OpenMP, CUDA, Triton); cloud computing (on hyper-scaler platforms, e.g., AWS, Azure, GCP); building machine learning models and pipelines in Python, using common libraries More ❯

versioning, testing, CI/CD, API design, MLOps) Building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., TensorFlow, MLFlow) Distributed computing frameworks (e.g., Spark, Dask) Cloud platforms (e.g., AWS, Azure, GCP) and HP computing Containerization and orchestration (Docker, Kubernetes) Strong problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly More ❯

apply, integrate and deploy Machine Learning capabilities and techniques into other systems. Are familiar with the Python data science stack through exposure to libraries such as Numpy , Scipy , Pandas, Dask , spaCy , NLTK, scikit-learn. Take pride in writing clean, reusable, maintainable and well-tested code. Demonstrate proficiency in automation, system monitoring, and cloud-native applications, with familiarity in AWS or More ❯

Experience working with large geospatial raster and vector datasets, including Earth Observation or climate data. Proficiency with processing large, complex datasets using Python-based geospatial tools (xarray, numpy, scipy, dask, geopandas, OGR, GDAL). Strong quantitative skills with the ability to extract insights from spatial data. Familiarity with modelling concepts from hydrology, atmospheric sciences, or environmental sciences. Desirable Skills Experience More ❯

collaborative, and dynamic environment. Nice to haves: Prior experience with PCB design, EDA tools, or related optimization problems. Hands-on experience in high-performance computing environments (e.g., Kubernetes, Ray, Dask). Contributions to open-source projects, publications, or top placements in ML competitions (e.g., Kaggle). Expertise in related fields such as Computer Vision, Representation Learning, or Simulation Environments. More ❯

collaborative, and dynamic environment. Nice to haves: Prior experience with PCB design, EDA tools, or related optimization problems. Hands-on experience in high-performance computing environments (e.g., Kubernetes, Ray, Dask). Contributions to open-source projects, publications, or top placements in ML competitions (e.g., Kaggle). Expertise in related fields such as Computer Vision, Representation Learning, or Simulation Environments. Our More ❯

decisions in the face of many nuanced trade offs and varied opinions. Experience in a range of tools sets comparable with our own: Database technologies: SQL, Redshift, Postgres, DBT, Dask, airflow etc. AI Feature Development: LangChain, LangSmith, pandas, numpy, sci kit learn, scipy, hugging face, etc. Data visualization tools such as plotly, seaborn, streamlit etc You are Able to chart More ❯

Triton, and/or CUDA code to achieve performance breakthroughs. Required Skills Understanding of Linux systems, performance analysis tools, and hardware optimisation techniques Experience with distributed training frameworks (Ray, Dask, PyTorch Lightning, etc.) Expertise with Python and/or C/C++ Development with machine learning frameworks (JAX, Tensorflow, PyTorch etc.) Passion for profiling, identifying bottlenecks, and delivering efficient solutions. More ❯