Our client, a leading hedge fund, is looking for a Senior Applied AI Researcher to join their Applied AI team in London. You will be transforming how professionals interact with artificial intelligence in high-performance environments. This team has developed a suite of AI-powered tools, including custom assistants with deep research capabilities, and is focused on building reasoning models and collaborative AI workspace. We are seeking a Senior Research Scientist to contribute to the development of next-generation AI tools that support decision-making and research workflows. You'll work on impactful projects that bring advanced machine learning research into production, directly influencing how users leverage AI in complex domains. This role offers a unique opportunity to push the boundaries of human + AI collaboration. You'll work within a rich ecosystem of data, models, compute resources, and expert users, enabling rigorous experimentation and innovation.
Responsibilities
Conduct original research in machine learning, particularly in reinforcement learning, agent architectures, and large language models (LLMs) Prototype and develop new ML models and algorithms for real-world deployment Collaborate with engineering teams to integrate research into production systems Design experiments and analyze results to evaluate model performance Engage with end-users to align research with practical applications Contribute to the research community through publications, presentations, and open-source projects Mentor junior researchers and help shape technical strategy Participate in the full research lifecycle from ideation to deployment
Requirements:
PhD in Computer Science, Machine Learning, Statistics, Applied Mathematics, or a related field Strong publication record in top-tier conferences (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP) 2-5 years of post-PhD experience Deep expertise in machine learning, with a focus on NLP and reinforcement learning Experience developing ML models in Python (PyTorch) Proven ability to translate research into scalable, practical solutions Experience deploying ML models in production environments Familiarity with financial markets or complex decision-making domains Knowledge of vector databases, semantic search, or information retrieval systems
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