1 to 25 of 64 PyTorch Jobs in the West Midlands

/ML techniques, including supervised, unsupervised, and reinforcement learning, deep learning, and large language models (LLMs). Experience with ML frameworks such as TensorFlow, PyTorch, and scikit-learn. Hands-on experience deploying ML models in production environments using MLOps principles. Expertise in relevant AI/ML applications, such as predictive More ❯

documented bids and face to face presentations. Experience of data science platforms (e.g. Databricks, Dataiku, AzureML, SageMaker) and machine learning frameworks (e.g. Keras, Tensorflow, PyTorch, scikit-learn) Cloud platforms - demonstrable experience of building and deploying solutions to Cloud (e.g. AWS, Azure, Google Cloud) including Cloud provisioning tools (e.g. Terraform). More ❯

role is for you if: Python for API and Model development (Machine learning frameworks and tooling e.g. Sklearn) and (Deep learning frameworks such as Pytorch and Tensorflow). Understanding of machine learning techniques. Experience with data manipulation libraries (e.g. Pandas, Spark, SQL). Git for version control. Cloud experience (we More ❯

of AI/ML concepts applied to Digital Twin, with extensive hands-on experience in training and fine-tuning models using frameworks such as PyTorch or TensorFlow Proficiency with Generative AI tools such as GPT, DALL-E, Gemini, and a solid understanding of underlying Machine Learning pipelines Exceptional analytical and More ❯

of AI/ML concepts applied to Digital Twin, with extensive hands-on experience in training and fine-tuning models using frameworks such as PyTorch or TensorFlow Proficiency with Generative AI tools such as GPT, DALL-E, Gemini, and a solid understanding of underlying Machine Learning pipelines Exceptional analytical and More ❯

internal best practices and the technical growth of the team. Required Qualifications Proficiency in Python and experience with machine learning libraries (e.g. scikit-learn, PyTorch, TensorFlow), Proven ability to deliver data science projects with measurable outcomes, Strong grounding in statistics, modelling, or applied mathematics, Experience with real-world data: cleaning More ❯

git, and github for collaboration and review). Machine/Deep Learning development skills, including experiment tracking (we use AWS SageMaker, Hugging Face, transformers, PyTorch, scikit-learn, Jupyter, Weights & Biases). An understanding of Language Models, using and training/fine-tuning and a familiarity with industry-standard LM families. More ❯

technical mentoring experience. Strong expertise in Python for machine learning (Pandas, NumPy, scikit-learn, etc.). Experience with deep learning frameworks such as TensorFlow, PyTorch, or JAX. Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model … Docker and container orchestration (e.g., Kubernetes). Preferred Skills: Experience with feature stores (e.g., Feast, Tecton). Knowledge of distributed training (e.g., Horovod, distributed PyTorch). Familiarity with big data tools (e.g., Spark, Hadoop, Beam). Understanding of NLP, computer vision, or time series analysis techniques. Knowledge of experiment tracking More ❯

technical mentoring experience. Strong expertise in Python for machine learning (Pandas, NumPy, scikit-learn, etc.). Experience with deep learning frameworks such as TensorFlow, PyTorch, or JAX. Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model … Docker and container orchestration (e.g., Kubernetes). Preferred Skills: Experience with feature stores (e.g., Feast, Tecton). Knowledge of distributed training (e.g., Horovod, distributed PyTorch). Familiarity with big data tools (e.g., Spark, Hadoop, Beam). Understanding of NLP, computer vision, or time series analysis techniques. Knowledge of experiment tracking More ❯

technical mentoring experience. Strong expertise in Python for machine learning (Pandas, NumPy, scikit-learn, etc.). Experience with deep learning frameworks such as TensorFlow, PyTorch, or JAX. Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model … Docker and container orchestration (e.g., Kubernetes). Preferred Skills: Experience with feature stores (e.g., Feast, Tecton). Knowledge of distributed training (e.g., Horovod, distributed PyTorch). Familiarity with big data tools (e.g., Spark, Hadoop, Beam). Understanding of NLP, computer vision, or time series analysis techniques. Knowledge of experiment tracking More ❯

technical mentoring experience. Strong expertise in Python for machine learning (Pandas, NumPy, scikit-learn, etc.). Experience with deep learning frameworks such as TensorFlow, PyTorch, or JAX. Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model … Docker and container orchestration (e.g., Kubernetes). Preferred Skills: Experience with feature stores (e.g., Feast, Tecton). Knowledge of distributed training (e.g., Horovod, distributed PyTorch). Familiarity with big data tools (e.g., Spark, Hadoop, Beam). Understanding of NLP, computer vision, or time series analysis techniques. Knowledge of experiment tracking More ❯

technical mentoring experience. Strong expertise in Python for machine learning (Pandas, NumPy, scikit-learn, etc.). Experience with deep learning frameworks such as TensorFlow, PyTorch, or JAX. Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model … Docker and container orchestration (e.g., Kubernetes). Preferred Skills: Experience with feature stores (e.g., Feast, Tecton). Knowledge of distributed training (e.g., Horovod, distributed PyTorch). Familiarity with big data tools (e.g., Spark, Hadoop, Beam). Understanding of NLP, computer vision, or time series analysis techniques. Knowledge of experiment tracking More ❯

technical mentoring experience. Strong expertise in Python for machine learning (Pandas, NumPy, scikit-learn, etc.). Experience with deep learning frameworks such as TensorFlow, PyTorch, or JAX. Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model … Docker and container orchestration (e.g., Kubernetes). Preferred Skills: Experience with feature stores (e.g., Feast, Tecton). Knowledge of distributed training (e.g., Horovod, distributed PyTorch). Familiarity with big data tools (e.g., Spark, Hadoop, Beam). Understanding of NLP, computer vision, or time series analysis techniques. Knowledge of experiment tracking More ❯

technical mentoring experience. Strong expertise in Python for machine learning (Pandas, NumPy, scikit-learn, etc.). Experience with deep learning frameworks such as TensorFlow, PyTorch, or JAX. Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model … Docker and container orchestration (e.g., Kubernetes). Preferred Skills: Experience with feature stores (e.g., Feast, Tecton). Knowledge of distributed training (e.g., Horovod, distributed PyTorch). Familiarity with big data tools (e.g., Spark, Hadoop, Beam). Understanding of NLP, computer vision, or time series analysis techniques. Knowledge of experiment tracking More ❯

Looking For: 2+ years of experience building and deploying machine learning models Strong programming skills in Python and ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch) Experience with model evaluation, tuning, and productionisation Exposure to cloud platforms (e.g., Azure, AWS, or GCP) and containerisation tools (e.g., Docker) A collaborative mindset, strong More ❯

as Python, R, or MATLAB. Experience with data wrangling, feature engineering, and preparing datasets for model validation. Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and model evaluation techniques. Experience with cloud platforms (e.g., AWS, Azure, GCP) and deployment of models in cloud environments. Experience with data visualization More ❯

timelines. And any experience of these would be a plus: Proficiency in Python programming and experience using AI-specific frameworks or libraries such as PyTorch, TensorFlow, LangChain, LlamaIndex, or similar tools; proficiency in SQL. Hands-on experience or strong theoretical knowledge of AI and GenAI techniques and methodologies. Familiarity with 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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

validation metrics. Proficiency in Python, R, or MATLAB. Experience with data wrangling, feature engineering, and dataset preparation. Familiarity with machine learning frameworks like TensorFlow, PyTorch, Scikit-learn. Experience with cloud platforms (AWS, Azure, GCP) and deploying models. Ability to use data visualization tools like Tableau or Power BI. Nice-to More ❯