Bayesian statistics, linear algebra and MVT calculus, advanced data modelling and algorithm design experience. Design and deployment experience using Tensor Flow, Spark ML, CNTK, Torch or Caffe. The perks A flexible environment that allows 1-2 days of remote working per week. 28 days holiday + a competitive pension More ❯
solutions at enterprise scale Data science experience, including data manipulation, model training, validation, and deployment Experience with AIML toolkits like MXNet, TensorFlow, Caffe, and Torch Our inclusive culture empowers us to deliver the best results. If you need workplace accommodations during the application or onboarding process, please visit this More ❯
Modern/Classical Control, Navigation, Data Fusion, Tracking and Guidance, Machine Learning Tools and Libraries such as Matlab, Simulink, Python, C/C++, Py Torch, Open AI-Gym/Universe, Model based design Experience of algorithm research and/or product development and support A keen curiosity about innovative More ❯
Modern/Classical Control, Navigation, Data Fusion, Tracking and Guidance, Machine Learning Tools and Libraries such as Matlab, Simulink, Python, C/C++, Py Torch, Open AI-Gym/Universe, Model based design Experience of algorithm research and/or product development and support A keen curiosity about innovative More ❯
of Continuous Integration (CI) pipelines. Complex deployments on AWS. Docker or comparable containerization technologies. Nice to have experience: Experience using numpy/pandas/torch/etc. Experience with Golang. Our salary range for the role is £70,000 to £130,000, depending on experience and interview performance. List More ❯
of model performance. You'll also work on re-implementing models in an efficient manner by using PyTorch and underlying technologies like Cuda Kernels, Torch compilation techniques. This would include: Evaluating and optimising compute resource usage (e.g., Hopper GPUs) for cost and time efficiency at training and inference times. More ❯
of model performance. You'll also work on re-implementing models in an efficient manner by using PyTorch and underlying technologies like Cuda Kernels, Torch compilation techniques. This would include: Evaluating and optimising compute resource usage (e.g., Hopper GPUs) for cost and time efficiency at training and inference times. More ❯
them. Good understanding of Docker and containerization. (Good to have) Experience with Pytorch and Python3, and comfortable with C++. (Good to have) Understanding of Torch script, ONNX runtime, TensorRT. (Good to have) Understanding of half-precision inference and int8 quantization. What we offer Company equity % in an early-stage More ❯
