1 to 25 of 47 Permanent Neural Network Jobs in London

you're passionate about pushing the boundaries of AI, we want you on board! Job Description We are seeking an experienced Neural Network Optimization Engineer who will specialize in enhancing the performance, latency, and throughput of neural network inference workflows. The ideal candidate will … ML researchers to ensure that our machine learning models run at peak efficiency and reliability in production environments. Key Responsibilities Optimize neural network models for inference performance and latency reduction Benchmark, analyze, and improve inference performance on targeted hardware platforms. Collaborate with the ML researchers to deploy … Stay updated with the latest developments in model optimization, inference engines, quantization methods, and related technologies. Requirements Proven professional experience optimizing neural network inference workloads. Strong expertise with TensorRT, Triton language, CUDA programming. Experience with neural network quantization techniques. Proficiency in Python and PyTorch. More ❯

flexibility, and efficiency to handle large algorithms. Algorithm Development: Develop and implement advanced AI algorithms and models, including machine learning, deep learning, and neural networks. Continuously evaluate and improve these algorithms to enhance system performance and accuracy. Data Integration: Identify relevant data sources and design methods for data … Implement evaluation methodologies to measure the performance and effectiveness of trained models. Fine-tune models based on feedback and data insights. Neural Network Design: Design and optimize deep learning neural networks for various AI tasks, such as natural language processing, computer vision, recommendation systems, and … field. Experience with large language models (LLMs) and prompt engineering. Experience in designing and developing AI systems, including machine learning, deep learning, and neural networks. Strong programming skills in languages such as Python, R, or Java Familiarity with AI libraries, frameworks, and tools such as TensorFlow, PyTorch, or More ❯

flexibility, and efficiency to handle large algorithms. Algorithm Development: Develop and implement advanced AI algorithms and models, including machine learning, deep learning, and neural networks. Continuously evaluate and improve these algorithms to enhance system performance and accuracy. Data Integration: Identify relevant data sources and design methods for data … Implement evaluation methodologies to measure the performance and effectiveness of trained models. Fine-tune models based on feedback and data insights. Neural Network Design: Design and optimize deep learning neural networks for various AI tasks, such as natural language processing, computer vision, recommendation systems, and … field. Experience with large language models (LLMs) and prompt engineering. Experience in designing and developing AI systems, including machine learning, deep learning, and neural networks. Strong programming skills in languages such as Python, R, or Java Familiarity with AI libraries, frameworks, and tools such as TensorFlow, PyTorch, or More ❯

and machine learning. Experience analyzing large data sets, data cleaning, and statistical analysis. Proven experience with at least three machine learning algorithms (e.g., neural networks, logistic regression, random forests). Proficiency with Java and Python, understanding of data structures, algorithms, and software design patterns. Experience with AI/ More ❯

ML tasks. Build and optimize tools for scalable probabilistic inference under memory, latency, and compute constraints. Apply and innovate on methods like Bayesian neural networks , variational autoencoders , diffusion models , and Gaussian processes for modern AI use cases. Collaborate closely with product, engineering, and business teams to build end More ❯

architectures.You will also need to be well-versed in various machine learning techniques, including supervised, unsupervised, and semi-supervised learning, as well as neural networks, support vector machines (SVM), tree-based methods, and deep learning algorithms. Knowledge in natural language understanding and generative AI will be essential.The role More ❯

You will also need to be well-versed in various machine learning techniques, including supervised, unsupervised, and semi-supervised learning, as well as neural networks, support vector machines (SVM), tree-based methods, and deep learning algorithms. Knowledge in natural language understanding and generative AI will be essential. The More ❯

Statsmodels, NumPy, SciPy, Matplotlib, TensorFlow, and Keras. Solid understanding of machine learning techniques, such as clustering, tree-based methods, boosting, text mining, and neural networks. Expertise in statistical modeling and techniques such as regression, hypothesis testing, simulation, resampling methods, and stratification. Degree in Data Science, Mathematics, Physics, Computer More ❯

like Tableau, Spotfire, Power BI etc. Expertise in predictive modelling, including both parametric (e.g. logit/probit) and non-parametric (e.g. random forest, neural net) techniques as well as wider ML techniques like clustering/random forest (desirable). Tech Stack: SQL, Python, R, Tableau, AWS Athena + More ❯

like Tableau, Spotfire, Power BI etc. Expertise in predictive modelling, including both parametric (e.g. logit/probit) and non-parametric (e.g. random forest, neural net) techniques as well as wider ML techniques like clustering/random forest (desirable). Tech Stack: SQL, Python, R, Tableau, AWS Athena + More ❯

computer science. Demonstrated experience with geospatial imagery, computer vision techniques and classification algorithms (e.g., semantic segmentation, object detection, pixel classification, random forest, convolutional neural networks). Experience with cloud platforms or geospatial pipelines (e.g., Google Earth Engine). Strong programming skills in (preferably) Python. Strong statistical background. Familiarity More ❯

computer science. Demonstrated experience with geospatial imagery, computer vision techniques and classification algorithms (e.g., semantic segmentation, object detection, pixel classification, random forest, convolutional neural networks). Experience with cloud platforms or geospatial pipelines (e.g., Google Earth Engine). Strong programming skills in (preferably) Python. Strong statistical background. Familiarity More ❯

complex subjects to non-technical stakeholders You are familiar with Terraform , Python , Pandas , and NumPy It is great if you have: Experience with Neural Networks/Deep Learning. Experience with information extraction, parsing, and segmentation. Experience with machine learning frameworks ( sklearn ) and ML workflow. Experience with NLP libraries More ❯

Computer Science, Machine Learning, or a closely related field. Machine learning expertise : Strong foundation in machine learning and deep learning algorithms (e.g., deep neural networks, supervised/unsupervised learning, predictive analysis, forecast modelling). Programming proficiency: Excellent Python programming skills with experience in developing and debugging production-level More ❯

Computer Science, Machine Learning, or a closely related field. Machine learning expertise : Strong foundation in machine learning and deep learning algorithms (e.g., deep neural networks, supervised/unsupervised learning, predictive analysis, forecast modelling). Programming proficiency: Excellent Python programming skills with experience in developing and debugging production-level More ❯

field Technical leadership with a range of technical abilities e.g. Data Engineering, Machine Learning, Cloud, DevOps & Software Good theoretical understanding of machine learning, neural networks, and other computer vision topics Hands-on experience with Python and PyTorch Hands-on experience with MLOps frameworks and platforms (We use ClearML More ❯

Science teams in an eCommerce or conversion rate optimisation-focused environment is a plus. Hands-on experience with Machine & Deep Learning, AI and Neural Networks tools including Python, Spark, Tensor Flow. Competencies across core programming languages including Python, Java, C/C++, R. Ability to work in a More ❯

Salary: £150-250k base + generous equity Are you excited to drive AI drug discovery forward by scaling SOTA models and optimizing neural nets on GPUs? Qualifications • Bachelor's/Master's or PhD in Computer Science, Engineering, or a related field • 5+ years of experience deploying More ❯

Salary: £150-250k base + generous equity Are you excited to drive AI drug discovery forward by scaling SOTA models and optimizing neural nets on GPUs? Qualifications • Bachelor's/Master's or PhD in Computer Science, Engineering, or a related field • 5+ years of experience deploying More ❯

Salary: £150-250k base + generous equity Are you excited to drive AI drug discovery forward by scaling SOTA models and optimizing neural nets on GPUs? Qualifications • Bachelor's/Master's or PhD in Computer Science, Engineering, or a related field • 5+ years of experience deploying More ❯

EC2, ECS, EKS, Lambda, SQS, SNS, RDS Aurora MySQL & Postgres, DynamoDB, EMR, and Kinesis. Solid engineering background in machine learning, deep learning, and neural networks. Experience with containerized stacks using Kubernetes or ECS for development, deployment, and configuration. Experience with Single Sign-On/OIDC integration and a More ❯

a strong background in machine learning, enjoy applying theory to develop real-world applications, with experience and expertise in bandit algorithms, LLMs, general neural networks, and/or other methods relevant to recommendation systems. You have hands-on experience implementing production machine learning systems at scale in Java More ❯

Bonus Experience: Databricks and PySpark Analysing blockchain data Building and maintaining data pipelines Deploying machine learning models Use of graph analytics and graph neural networks Following funds on chain by using block explorers. Job Benefits Hybrid working and the option to work from almost anywhere for up to More ❯

at the forefront of developing advanced Machine Learning solutions for autonomous driving. Our team tackles groundbreaking challenges in designing state-of-the-art neural networks, pioneering innovative end-to-end architectures, and advancing ML techniques in perception, prediction, and motion planning. We're passionate about pushing the boundaries More ❯

a positive. An understanding of machine learning processes and their applications to investments. Ideally, they will be familiar with various algorithms (logistic regression, neural networks) and an understanding of how to implement these in Python. A passion for bringing together investment ideas in an organized framework with the More ❯