optimization, scenario analysis, and statistical methodologies. Strong grasp of supervised/unsupervised methods, evaluation metrics, feature engineering, and model tuning. Proficiency in Python (pandas, NumPy, Scikit-learn); experience with PyTorch or TensorFlow for deep learning. Experience with API development and connecting AI systems to external platforms. Working knowledge in deep learning techniques, including CNNs, RNNs, and transformers. Hands-on experience More ❯
experience leading projects or teams is a plus for a lead role Programming & ML Skills: Advanced programming skills in Python (including libraries such as pandas, scikit-learn, TensorFlow/PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps: Proven experience with AWS cloud More ❯
focusing on ML and AI. Proven expertise in deploying and managing Generative AI models (e.g., GPT, Stable Diffusion, BERT). Proficient in Python and ML libraries such as TensorFlow, PyTorch, or Hugging Face. Skilled in cloud platforms (AWS, GCP, Azure) and managed AI/ML services. Hands-on experience with Docker, Kubernetes, and container orchestration. Expertise with Databricks, including ML More ❯
LSTM), and generative models (GAN, VAE) to enhance predictive accuracy, interpretability, and automation. Engineer scalable analytical frameworks and reusable ML assets, integrating Python-based (or other) ML pipelines (TensorFlow, PyTorch, Scikit-learn, Pandas) with enterprise data platforms (Snowflake, Azure, Google Vertex AI) to standardise insight generation and model delivery. Collaborate with Data Architecture and Engineering to operationalise models through containerised More ❯
production deployments. What We're Looking For: Essential: Experience with LLMs (GPT, BERT) and NLP tasks. Fine-tuning pre-trained models for domain-specific applications. Strong Python skills (OOP, PyTorch, Hugging Face, scikit-learn, Pandas, NumPy). Deploying models with Docker, Kubernetes, or serverless platforms. Familiarity with CI/CD, MLOps, and cloud platforms (AWS preferred). Desirable: Named entity More ❯
feature engineering and model design to validation, deployment, and monitoring. Technical Proficiency: Fluency in Python, SQL, or similar languages, and experience with deep learning frameworks such as TensorFlow, Keras, PyTorch, or MXNet. Agile & DevSecOps: Familiarity with Agile methodologies and DevSecOps practices, including Git for version control. Cloud Platforms: Exposure to Azure, AWS, or GCP, with a strong interest in building More ❯
advanced AI architectures. You are an industry expert in ML model development, deployment, and MLOps at scale. You are deeply comfortable with Python, SQL, and ML frameworks (e.g., TensorFlow, PyTorch) and have experience working in a microservices architecture (Go Lang experience is a plus). You have extensive experience with real-time ML applications, reinforcement learning, graph-based models, and More ❯
Who You Are 2+ Programming experience, from either an engineering role, a computer science degree, or personal projects Python proficiency Experience using any of the following: Keras, Tensorborad, Tensorflow, Pytorch, Pytorch Lightning, Jupyter notebooks, colabs, matplotlib and other ML frameworks and tools A passion for connecting with real users and enabling them to be power users of the product Good More ❯
Face Transformers, OpenAI API, LangChain, or similar Experience fine-tuning large transformer models or implementing retrieval-augmented generation systems Strong Python programming skills and familiarity with ML libraries (e.g., PyTorch, TensorFlow) Knowledge of prompt engineering best practices and prompt optimization Understanding of LLM evaluation methods, including human-in-the-loop and automated metrics Familiarity with deploying LLMs in cloud or More ❯
of applied machine learning projects, ideally across commercial or regulated sectors Strong Python skills and comfort using core libraries (e.g. NumPy, Pandas), plus familiarity with deep learning tooling like PyTorch Expertise in a wide range of ML methods, including supervised and unsupervised learning, time series, or NLP and LLM/GenAI based projects. Ability to scope and structure solutions around More ❯
Experience with game engine audio implementation and middleware (e.g., Wwise, FMOD Studio, Unreal MetaSounds) Understanding of DSP and audio signal processing Hands-on experience integrating machine learning models (TensorFlow, PyTorch, ONNX) into production pipelines for tasks such as inference, data processing, and generative workflows Experience debugging code across various development environments Experience managing collaboration tools and version control systems (e.g. More ❯
technical discipline (e.g., Maths, Statistics, Computer Science, Physics, Chemistry, Biomedical Engineering). Experience in embodied AI, world models or robotics. Python programming in a modern deep learning framework, e.g. PyTorch or JAX. Familiar with deep learning fundamentals: models, optimisation, evaluation and scaling. Capable of designing, executing and reporting from ML experiments. Mathematics skills to support the above: calculus, probability theory More ❯
proven responsibility for system architecture, mentoring junior team members, and conducting thorough code reviews Strong programming skills in Python and C++, with experience using libraries and frameworks such as PyTorch, NumPy, Pandas, TensorFlow, and OpenCV for computer vision and data processing Familiarity with front-end technologies including JavaScript and HTML for building user-facing interfaces or tools Practical, hands-on More ❯
related quantitative field), or equivalent industry research experience. • Ideally 3+ years in machine learning/deep learning/LLMs or adjacent advanced AI research. • Proficient in Python; experience with PyTorch or JAX. • Understanding of reinforcement learning, multi-agent systems, probabilistic/Bayesian methods, or large-scale training. • Curiosity-driven mindset, strong problem solving, and the ability to work cross-functionally. More ❯
publications); Able to demonstrate your coding skills (Python, C++), for example through projects or previous work experience, and have experience of algorithm implementation in machine learning frameworks such as PyTorch; Passionate about the field of AI, both theory and practice, with a keen interest to continue learning. We are excited to hear about your research! Please provide a short summary More ❯
UCX, or custom collective communication implementations. Familiarity with HPC networking protocols/libraries such as RoCE, Infiniband, Libibverbs, and libfabric. Experience with distributed deep learning/MoE frameworks, including PyTorch Distributed, vLLM, or DeepEP. Solid understanding of deploying and optimizing large-scale distributed deep learning workloads in production environments, including Linux, Kubernetes, SLURM, OpenMPI, GPU drivers, Docker, and CI/ More ❯
participate in the upside of an ultra-growth venture. Have fun Apply if: You have experience with our stack: Python, FastAPI, Postgres, SQLAlchemy, Alembic, Typescript, React, LlamaIndex/LangChain, PyTorch, HuggingFace, OpenAI, Docker, Azure, You have taken entire products or features from ideation to deployment and you've measured their impact. You enjoy diving deep into the domain, understanding the More ❯
learning models, particularly for sequential data (e.g. time series, language) using techniques such as LSTMs or transformers. Hands-on experience with modern Machine/Deep Learning frameworks such as PyTorch, TensorFlow, or Hugging Face Transformers. Familiarity with both pre-training and fine-tuning of large-scale models. Experience working with structured and unstructured data, such as text, logs, or time More ❯
Working closely with researchers and product engineers to ship fast in an environment that iterates constantly What they're looking for Strong Python engineering background Experience with frameworks like PyTorch, JAX or TensorFlow Demonstrated ability to scale ML workflows in real-world settings (cloud, GPU clusters, distributed training) Comfortable with MLOps tooling (W&B, Ray, Docker, etc.) Familiar with modern More ❯
Scientist team consists of about twenty machine learning scientists. The team is supported by an ML Ops team that provides state-of-the-art tooling (including AWS, Encord, Ray, PyTorch Lightning and Weights & Biases). The Applied Science team works closely with product engineering to deploy models to serve our worldwide customer base. Position Overview: We are looking for a More ❯
We're looking for three talented Principal Data Scientists and one Senior Data Scientist to join our supportive and forward-thinking Commercial Data team. Each role offers a unique opportunity to make a real impact and shape how we use More ❯
Work where work matters. Elevate your career at Qodea, where innovation isn't just a buzzword, it's in our DNA. We are a global technology group built for what's next, offering high calibre professionals the platform for high More ❯
of taking responsibility for projects from conception over implementation to production use Strong experience in Python, including its bindings to native code in C++ or Rust Deep familiarity with PyTorch and the AI software ecosystem. Key technologies include the PyTorch Distributed library, large-scale training frameworks like Megatron-LM or TorchTitan and highly-optimized inference libraries such as TensorRT-LLM More ❯
quickly validated. Technical Experience: Strong software engineering skills with experience in machine learning, numerical computing, and/or applied ML research Proficiency with Python and numerical + ML frameworks (PyTorch, JAX, numpy), familiarity with PyTorch 2.x compiler stack (e.g., TorchScript, TorchInductor) is a plus Practical experience deploying ML models or infrastructure in a research and production environment BS/MS More ❯
Founding Research/ML Engineer London Up to £180K + Generous equity! VC Backed Start Up My client is building an AI material scientist that goes end-to-end from hypothesis generation to testing in their own high-throughput lab. More ❯
