1 to 25 of 182 Permanent PyTorch Jobs

deliver superior software solutions. Skills Required: In depth experience designing & building backend applications in Python. Familiarity with Big Data and Machine Learning technologies (NumPy, PyTorch, TensorFlow, Spark). Experience developing in a highly Agile/Scrum environment. Familiarity with CICD, containerisation, deployment technologies & cloud platforms (Jenkins, Kubernetes, Docker, AWS). more »

Job DescriptionMachine Learning Infrastructure EngineerPermLondon office 4 days per week Can offer sponsorship and relocationUp to £200KA YC-based start-up building the next-generation of helpful and honest AI is looking for a Machine Learning Infrastructure Engineer to join more »

use of generative models.Skilled in Python, with a willingness to adapt to other programming languages and tools as needed.Experienced with deep learning platforms like PyTorch, and able to quickly learn new technologies. more »

Job DescriptionJoin a leader in generative AI technologies, who have recently secured Series A funding to advance our work in digital avatars and human clones. Role: MLOps EngineerLocation: LondonSalary: Up to £100,000Responsibilities:Develop ML Pipelines: Build and maintain scalable more »

AI technology.Interested? Apply directly through LinkedIn, or send your CV to george@eu-recruit.comKey Words: Machine Learning/LLM/Large Language Model/PyTorch/High Performance Computing/HPC/GPU/TPU/Deepspeed/AI/OpenAI/Distributed SystemsBy applying to this role, you understand more »

for deep learning framework (TF-light, GGML, ONNX, ...) Excellent Python programming skills Expert in at least one popular machine learning framework such as Pytorch Knowledge of real-time audio signal processing and/or real-time machine learning Proven experience of applying and optimising AI/ML techniques and more »

Data Science & ML Consultant Location: London – Hybrid Employment: Full Time All applicants must hold a DV (Developed Vetting) clearance to apply. The Opportunity: For the Ministry of Defence (MOD), maintaining national security is a 24-hour-a-day, 365-day more »

large and complex data sets to extract insights and identify trends Advanced programming skills with Python, including experience with Jupyter notebooks, PyTest, Pandas, SciKit, PyTorch, type-checking, functional programming, CI/CD, and Git A strong background in machine learning for customer and marketing purposes around pricing, promotions, recommendations, forecasting more »

complex machine learning solutions. Expertise in product experimentation, Causal AI, and advanced statistical techniques. Deep knowledge of data science tools (e.g., scikit-learn, TensorFlow, PyTorch) and big data technologies (e.g., Spark). Proficiency in Python for data manipulation, model building, and scripting. Strong communication skills to present findings to both more »

Learning, or a related field. Experience in AI/Machine Learning research and development. Proficiency in Python. Experience with popular machine learning frameworks (TensorFlow, PyTorch, scikit-learn). Experience with using NVIDIA GPUs for fine tuning AI models Strong mathematical and statistical background. Excellent problem-solving and critical-thinking skills. more »

scientists, engineers, and designers in the company. Machine Learning Engineer - Ideal skillset would include: Top Academics in a relevant field Strong knowledge of TensorFlow, PyTorch, Keras and Scikit-Learn Ideally 1+ year professional experience as a Machine Learning Engineer Research minded and experimental approach to problem solving This is a more »

dataset deployment on cloud infrastructure. What We Value: 💡 Bring expertise in machine learning, NLP, and deep learning. We are looking for extensive skills in PyTorch, Python, and large-scale dataset processing are essential. Bonus points for AWS/GCP, Docker, and platform familiarity. You must have developed a product and more »

use and admin. Experience deploying cloud services (AWS is a bonus). Experience with Docker and Kubernetes. Using frameworks such as AirFlow. ML background - PyTorch for computer vision. This is a fully remote role which comes with: Budget for WFH set up. Stock options. 25 days annual leave. This role more »

for deploying these models. Experience of working within a collaborative multi-functional team environment. Proficient in Python, and familiar with deep learning frameworks like PyTorch (required) and Tensorflow. Skilled in using cloud services, containerisation, handling distributed systems, and familiar with continuous integration, unit testing, and code review practices. Experience in more »

in a tech team using a diverse tech stack including: Backend: Python, FastAPI, PostgreSQL, Vespa, SQLAlchemy, Flask. Frontend: React, Next.js. Data Science: Python, Jupyter, PyTorch, Pandas, Spacy, Huggingface, Numpy, Streamlit, Weights and biases. Infra: Pulumi, Docker, AWS AppRunner, Step Functions, Grafana cloud monitoring, Prefect. Who you are Must haves: Experience more »

We are looking for a Senior Machine Learning Engineer to join our new Knowledge Enrichment team at BenchSci. You will help design and implement ML-based approaches to analyse complex biomedical data such as experimental protocols and results from several more »

problems and solid knowledge of mathematical foundations of ML algorithms. Proficiency in Deep Learning Architectures (e.g., MLP, RNN, CNN) and popular frameworks (e.g., TensorFlow, PyTorch, Keras). Expertise in multiple open-source machine learning libraries (e.g., scikit-learn, Pandas, NumPy, Matplotlib, seaborn, Spacy, NLTK, transformers, Hugging Face, pymupdf). Practical more »

model development and deployment. Excellent communication and teamwork skills, capable of collaborating effectively with non-technical colleagues. Strong knowledge of Python and ML libraries (PyTorch, Tensorflow, Keras, etc.) with R/SQL knowledge, partnered with experience with MLOps tools (Kubernetes, Docker, Jenkins, etc.) would be advantageous. Strong knowledge and experience more »

language Demonstrated knowledge of RAG (Retrieval Augmented Generation), Zeroshot (ZSL), Multishot prompting and other prompt engineering approaches, common AI/ML frameworks (e.g., TensorFlow, PyTorch), GIT, Docker and vector databases Demonstrated knowledge of cloud AI services (AWS SageMaker, GCP AI Platform, Azure ML) and APIs Proficiency in using and configuring more »

language Demonstrated knowledge of RAG (Retrieval Augmented Generation), Zeroshot (ZSL), Multishot prompting and other prompt engineering approaches, common AI/ML frameworks (e.g., TensorFlow, PyTorch), GIT, Docker and vector databases Demonstrated knowledge of cloud AI services (AWS SageMaker, GCP AI Platform, Azure ML) and APIs Proficiency in using and configuring more »

language Demonstrated knowledge of RAG (Retrieval Augmented Generation), Zeroshot (ZSL), Multishot prompting and other prompt engineering approaches, common AI/ML frameworks (e.g., TensorFlow, PyTorch), GIT, Docker and vector databases Demonstrated knowledge of cloud AI services (AWS SageMaker, GCP AI Platform, Azure ML) and APIs Proficiency in using and configuring more »

language Demonstrated knowledge of RAG (Retrieval Augmented Generation), Zeroshot (ZSL), Multishot prompting and other prompt engineering approaches, common AI/ML frameworks (e.g., TensorFlow, PyTorch), GIT, Docker and vector databases Demonstrated knowledge of cloud AI services (AWS SageMaker, GCP AI Platform, Azure ML) and APIs Proficiency in using and configuring more »

language Demonstrated knowledge of RAG (Retrieval Augmented Generation), Zeroshot (ZSL), Multishot prompting and other prompt engineering approaches, common AI/ML frameworks (e.g., TensorFlow, PyTorch), GIT, Docker and vector databases Demonstrated knowledge of cloud AI services (AWS SageMaker, GCP AI Platform, Azure ML) and APIs Proficiency in using and configuring more »

language Demonstrated knowledge of RAG (Retrieval Augmented Generation), Zeroshot (ZSL), Multishot prompting and other prompt engineering approaches, common AI/ML frameworks (e.g., TensorFlow, PyTorch), GIT, Docker and vector databases Demonstrated knowledge of cloud AI services (AWS SageMaker, GCP AI Platform, Azure ML) and APIs Proficiency in using and configuring more »

language Demonstrated knowledge of RAG (Retrieval Augmented Generation), Zeroshot (ZSL), Multishot prompting and other prompt engineering approaches, common AI/ML frameworks (e.g., TensorFlow, PyTorch), GIT, Docker and vector databases Demonstrated knowledge of cloud AI services (AWS SageMaker, GCP AI Platform, Azure ML) and APIs Proficiency in using and configuring more »