2 of 2 Deep Learning Jobs in Edinburgh

About the Role Are you passionate about building AI systems that can think, learn, and trade autonomously? Predictiva is looking for a Deep Learning Engineer to join our award-winning team developing next-generation AI trading platforms. You’ll work on cutting-edge models, transform research into production systems, and help redefine how machine learning interacts with … global financial markets. Key Responsibilities As a Deep Learning Engineer at Predictiva, you will: Develop and train advanced deep learning models using Python and frameworks such as PyTorch or TensorFlow. Build and deploy APIs for AI models, preferably using FastAPI, and deploy them on cloud platforms (Azure, GCP, or AWS). Integrate model training pipelines with … visualization, and performance monitoring. Prepare and process data — including structured and unstructured time-series data — for use in ML models. Bridge the gap between research and production, translating machine learning ideas into real-world trading applications. Collaborate across teams, sharing knowledge and presenting insights to data scientists, software engineers, and financial experts. About Predictiva Predictiva is an AI FinTech More ❯

adversarial attacks and enhance generalization performance by exposing the model to perturbed input examples during training. Supervised Fine tuning (SFT): Develop and execute SFT strategies for specific tasks. Reinforcement Learning from Human Feedback (RLHF): Run and refine RLHF pipelines to align models with human preferences. Pruning: Design and implement model pruning strategies to reduce model size and computational complexity … deploy AI powered products. Contribute to the development of internal tools and infrastructure for model training and evaluation. Qualifications Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field and 5+ years of relevant work experience or 7+ years of relevant work experience. Strong programming skills in Python and experience with deep learning frameworks like PyTorch. Solid understanding of machine learning principles, including supervised learning, unsupervised learning, and reinforcement learning. Proven experience in designing and conducting experiments, analyzing data, and drawing meaningful conclusions. Familiarity with large language models, transformer architectures, and related concepts. Experience with data processing tools and techniques (e.g., Pandas, NumPy). Experience working with Linux systems More ❯