You'll Bring: Degree in Computer Science, AI, ML, or a related field. Experience in developing and deploying AI/ML solutions. Proficiency in Python and ML frameworks (TensorFlow, PyTorch). Strong understanding of LLMs, NLP, and machine learning algorithms. MLOps knowledge and experience with version control systems. Back-end engineering skills in Python or Node.js (willingness to upskill in More ❯
deep learning algorithms including the latest multimodal foundation models and generative AI frameworks' exploration, development, and implementation. Extensive experience with common machine learning Python frameworks such as TensorFlow and PyTorch; and Python libraries such as pandas, and computer vision libraries such as OpenCV. Experience in ONNX and TensorRT. Very comfortable working in Linux environment. Familiarity with software development tools and More ❯
deployment of ML models into production environments. Expertisein Python and relevant ML libraries Experience with various ML algorithms (e.g. supervised, unsupervised, reinforcement learning) and deep learning frameworks (e.g. TensorFlow,PyTorch). Experience developing generative AI applications and deploying them into production. Experience working with cloud platforms, ideally Google Cloud Platform (GCP) andBigQuery. Proficiencyin working with and querying global scale datasets. More ❯
You: Experience: 5+ years of experience in machine learning engineering, preferably in Ad Tech, Mar Tech, or related high-scale environments. Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn). Deep understanding of supervised, unsupervised, and reinforcement learning methods. Experience with GCP services such as Vertex AI, BigQuery ML, Dataflow, AI Platform Pipelines, and Dataproc. Solid knowledge More ❯
industry. Expert-level proficiency in Python and extensive experience with data science libraries such as Pandas, NumPy, and Scikit-learn. Deep hands-on experience with deep learning frameworks, particularly PyTorch . Proven experience with statistical analysis , hypothesis testing, and data visualization techniques. Familiarity with computer vision tasks and concepts, with direct experience working with YOLO models being a major plus. More ❯
industry. Expert-level proficiency in Python and extensive experience with data science libraries such as Pandas, NumPy, and Scikit-learn. Deep hands-on experience with deep learning frameworks, particularly PyTorch . Proven experience with statistical analysis , hypothesis testing, and data visualization techniques. Familiarity with computer vision tasks and concepts, with direct experience working with YOLO models being a major plus. More ❯
applying deep learning and generative AI techniques, including implementing custom architectures, pretraining/fine-tuning generative models, building agentic workflows, and deploying solutions with real-world constraints. Familiarity with PyTorch, TensorFlow, JAX or similar frameworks Preferred Qualifications Experience in building AI agents (based on RL, LLMs, etc.) Experience in building multi-agent simulation Background in Large Language Models/NLP More ❯
industry. Expert-level proficiency in Python and extensive experience with data science libraries such as Pandas, NumPy, and Scikit-learn. Deep hands-on experience with deep learning frameworks, particularly PyTorch . Proven experience with statistical analysis , hypothesis testing, and data visualization techniques. Familiarity with computer vision tasks and concepts, with direct experience working with YOLO models being a major plus. More ❯
industry. Expert-level proficiency in Python and extensive experience with data science libraries such as Pandas, NumPy, and Scikit-learn. Deep hands-on experience with deep learning frameworks, particularly PyTorch . Proven experience with statistical analysis , hypothesis testing, and data visualization techniques. Familiarity with computer vision tasks and concepts, with direct experience working with YOLO models being a major plus. More ❯
london (city of london), south east england, united kingdom
Ultralytics
industry. Expert-level proficiency in Python and extensive experience with data science libraries such as Pandas, NumPy, and Scikit-learn. Deep hands-on experience with deep learning frameworks, particularly PyTorch . Proven experience with statistical analysis , hypothesis testing, and data visualization techniques. Familiarity with computer vision tasks and concepts, with direct experience working with YOLO models being a major plus. More ❯
Including you. What you'll need Proven experience in leading data science projects in cybersecurity, fraud, or behavioural analytics. Data Engineering - Deep expertise in Python, ML frameworks (e.g. TensorFlow, PyTorch), and cloud platforms (preferably GCP). Deep Learning Theory & Applications - Strong grasp of anomaly detection, time-series modelling, and deep learning architectures. Experience with LLMs and generative AI in production More ❯
for the last 10 years. You must be able to hold or gain a UK government security clearance. Preferred technical and professional experience Experience with machine learning frameworks (TensorFlow, PyTorch, scikit-learn). Familiarity with big data technologies (Hadoop, Spark). Background in data science, IT consulting, or a related field. AWS Certified Big Data or equivalent. IBM is committed More ❯
CD pipelines (GitLab), and containerization (Docker). Strong Python programming skills: Strong programming skills in Python, including its ecosystem for AI/ML, with experience using libraries such as PyTorch, Hugging Face Transformers, scikit-learn, and TensorFlow to build and implement AI-driven solutions. Product mindset: Ability to think from the user's perspective and design solutions that align with More ❯
CD pipelines (GitLab), and containerization (Docker). Strong Python programming skills: Strong programming skills in Python, including its ecosystem for AI/ML, with experience using libraries such as PyTorch, Hugging Face Transformers, scikit-learn, and TensorFlow to build and implement AI-driven solutions. Product mindset: Ability to think from the user's perspective and design solutions that align with More ❯
CD pipelines (GitLab), and containerization (Docker). Strong Python programming skills: Strong programming skills in Python, including its ecosystem for AI/ML, with experience using libraries such as PyTorch, Hugging Face Transformers, scikit-learn, and TensorFlow to build and implement AI-driven solutions. Product mindset: Ability to think from the user's perspective and design solutions that align with More ❯
current with emerging techniques. You are experienced in multivariate testing, mentoring junior scientists, and leading technical decisions. You are proficient in Python, Java, Scala, and ML frameworks (e.g., TensorFlow, PyTorch ), with experience in cloud platforms (AWS), big data (Spark), and deployment tools (Kubernetes, Airflow, Docker). Accommodation requests If you need assistance with any part of the application or recruiting More ❯
Working with Data Scientists to deploy trained machine learning models into production environments Working with a range of models developed using common frameworks such as Scikit-learn, TensorFlow, or PyTorch Experience with software engineering best practices and developing applications in Python. Technical experience of cloud architecture, security, deployment, and open-source tools ideally with one of the 3 major cloud More ❯
Computing, Computer Vision, or related technical discipline. Good knowledge of machine learning and computer vision algorithms. Familiarity with real-time imaging pipelines. Experience with scientific software packages such as PyTorch, OpenCV, Pandas, SciPy, NumPy, SciKit's, etc. Experience in standard software engineering practices including version control systems and software testing methodologies. Excellent oral and written communication skills. Analytical thinker, attentive More ❯
this is essential as you'll be the domain expert from day one. Excellent Python programming skills and strong familiarity with ML libraries such as scikit-learn, XGBoost, TensorFlow, PyTorch, Keras etc. Proven track record deploying ML models to production (API, batch, or streaming contexts) Solid understanding of the modern software engineering, infrastructure and data technologies Experience working with LLMs More ❯
ability to collaborate effectively across departments Strong project management and organisational skills Proficiency in programming languages such as Python, Java, or Scala, and experience with ML frameworks like TensorFlow, PyTorch, or scikit-learn Experience with large-scale distributed systems and big data technologies (e.g., Spark, Hadoop, Kafka) Accommodation requests If you need assistance with any part of the application or More ❯
Including you. What you'll need Proven experience in leading data science projects in cybersecurity, fraud, or behavioural analytics. Data Engineering - Deep expertise in Python, ML frameworks (e.g. TensorFlow, PyTorch), and cloud platforms (preferably GCP). Deep Learning Theory & Applications - Strong grasp of anomaly detection, time-series modelling, and deep learning architectures. Experience with LLMs and generative AI in production More ❯
how of designing and implemention synchronous, asynchronous and batch data processing operations Expert level programming skills in Python, along with experience in using relevant tools and frameworks such as PyTorch, FastAPI and Huggingface; strong programming skills in Java are a plus Expert level know-how of ML Ops systems, data pipeline design and implementation, and working with ML platforms (preferably More ❯
Engineering, or related technical field 2+ years of experience in AI/ML development with a focus on practical applications Strong proficiency in Python and relevant AI libraries (TensorFlow, PyTorch, Hugging Face) Hands-on experience with workflow automation platforms like N8N, AirTable, and proven track-record. Experience with AI agent development and testing methodologies using Google ADK, LangGraph, Llamaindex Understanding More ❯
Scientific publications in top-tier AI and neuroscience conferences (NeurIPS, ICLR, ICML, AAAI, CVPR, Cosyne, SFN, CNN ecc) or peer reviewed journals Familiarity with deep learning libraries such as Pytorch, Huggingface, Transformers, Accelerator and Diffuser. Hands-on experience in training and fine-tuning generative models like diffusion models or large language models such as GPTs and LLAMAs. Experience with data More ❯
experience in audio processing libraries (e.g., librosa, torch audio, or similar). Must understand machine learning techniques, including deep learning architectures (e.g., CNNs, RNNs, GANs) and relevant frameworks (e.g., PyTorch). Must be proficiency in data preprocessing, feature extraction, and data augmentation techniques for audio. Expected to have strong problem-solving skills and ability to think creatively to devise innovative More ❯
