17 of 17 Permanent PyTorch Jobs in Edinburgh

lifecycle of AI projects, machine learning operationalization processes Experienced with AI solutions deployment to main cloud providers Hands-on with Python and TensorFlow or PyTorch Possessing strong interpersonal, analytical, and problem-solving skills Easily translating complex concepts in clear, concise, and meaningful ways, that a non-technical audience can easily More ❯

to fine-tune those models eg. XGBoost, Deep Neural Networks, Transformers, Markov chains, etc. Experience using specialized machine learning libraries e.g. Fastai, Keras, Tensorflow, pytorch, sci-kit learn, huggingface, etc. Must demonstrate the capacity of reading, understanding and implementing new techniques in the field of machine learning as they emerge. More ❯

in both machine learning and software engineering. Experience deploying machine learning models in production environments. Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, scikit-learn). Familiarity with cloud platforms like GCP, AWS, or Azure. Experience with CI/CD pipelines for machine learning (e.g., Vertex AI). More ❯

ML platform development and model deployment Experience in establishing and improving MLOps processes Strong Python development skills Experience with a machine learning toolkit, preferably PyTorch Personal qualities & attributes Strong collaboration and communication skills, with the ability to work effectively in a multidisciplinary team and convey technical concepts to a variety More ❯

and machine learning applied to audio. Proficiency in programming languages such as Python, C++, and MATLAB. Hands-on experience with deep learning frameworks (TensorFlow, PyTorch, etc.). Understanding of real-time and embedded audio processing techniques. Experience working with generative AI models for audio synthesis and transformation. Strong problem-solving More ❯

for reliability and cost, or improving our search service Tech Stack: Platform: AWS, Pulumi, Docker, Prefect, Github actions, Grafana cloud monitoring Data Science: Python, PyTorch, Pandas, Spacy, Huggingface, Numpy, Streamlit, Argilla, Weights and Biases Backend: Python, FastAPI, PostgreSQL, Vespa, SQLAlchemy, AWS Batch, Lambda, S3 Frontend: React, Next.js Key skills and More ❯

Graphs Experience with traditional ML models and feature engineering. Experience with LLMs. Strong programming skills (e.g., Python) and experience with modern ML frameworks (e.g., PyTorch, TensorFlow, LangChain). Collaborating with other Researchers, Product, Engineering and Business Stakeholders in an agile manner to demonstrate value and iterate with customer feedback. For More ❯

experience in ML, AI, CS, physics, or mathematics • Strong publication record (NeurIPS, ICML, ICLR, etc.) • Proficient in Python, C++, and deep learning frameworks (e.g., PyTorch, TensorFlow) • Solid grasp of ML techniques; independent and team-oriented mindset Preferred: • Experience in LLMs, reinforcement learning, or chip design • Familiarity with JAX and optimization More ❯

solutions using Azure Machine Learning services. Have a knowledge of top ML libraries scalable across multiple CPU’s and GPU’s. Such as TensorFlow, PyTorch, Keras, OpenAI, XGBoost. And have used these in a commercial setting. Troubleshoot and resolve issues related to data processing and AI model performance. Collaborate with More ❯

/CPU/TPU architecture Vector/Matrix/Scaler units simulation/modelling - System C/TLM/UML ML frameworks - Tensorflow/PyTorch/Python or similar DSP algorithms - FIR/IIR filters or FFT... IP design, system design, system architecture low-power, performance and optimisation Applicants must More ❯

the boundaries of what’s possible. Nice to have experience in: Reinforcement learning Large Language Models (LLMs) Optimisation methods Deep learning frameworks (e.g., TensorFlow, PyTorch) Applied AI research at scale What’s on offer: Hybrid remote work with offices in Cambridge or London The chance to publish and share your More ❯

data handling, visualization , and biological data analysis. Proficient in Python , familiar with software development best practices . Practical experience with TensorFlow and/or PyTorch . Preferred Qualifications 3+ years post-graduate experience in academia or biotech/pharma, applying ML/AI to biological datasets . Prior exposure to More ❯

solutions using Azure Machine Learning services. Have a knowledge of top ML libraries scalable across multiple CPU’s and GPU’s. Such as TensorFlow, PyTorch, Keras, OpenAI, XGBoost. And have used these in a commercial setting. Troubleshoot and resolve issues related to data processing and AI model performance. Collaborate with More ❯

teams in AI tech. What They’re Looking For Solid AI/ML experience with a strong grasp of Python & frameworks like TensorFlow/PyTorch . Strong back-end engineering skills (Python or Node.js, bonus points for React/front-end experience). Experience deploying AI models into real-world More ❯

training, fine-tuning, and inference APIs. Requirements 5+ years of experience building and deploying machine learning systems in production environments. Proficiency in Python and PyTorch, with a strong understanding of transformer architectures, LLMs, and multimodal generative models. Expertise in distributed training frameworks like DeepSpeed or Fully Sharded Data Parallel (FSDP More ❯

MACHINE LEARNING ENGINEER (MLOPS) HYBRID/EDINBURGH OR UK REMOTE £55-60,000 PLUS BENEFITS Based in Edinburgh, GRW Talent’s client is considered to be the leading audio-driven facial animation provider in the video-game industry. They employ More ❯

LLVM or MLIR Work with hardware teams to ensure the software stack fully leverages the capabilities of our OTPU architecture Extend ML frameworks (e.g. PyTorch, ONNX, OpenXLA) to better support performance-critical inference paths Lead design reviews, mentor engineers, and promote best practices in HPC and performance engineering Stay on … sensitive applications Hands-on experience with ML compilers (e.g. LLVM, MLIR), and knowledge of runtime and scheduling optimisations Practical knowledge of ML frameworks like PyTorch, ONNX, or OpenXLA, and how to optimise their execution Experience scaling AI workloads across clusters or custom infrastructure—not just deploying on standard cloud setups More ❯