Quantitative Developer | AI​/ML Engineer | Quant Firm

Our client is a global investment firm with a strong track record across a range of strategies. They're growing their machine learning and AI capabilities and are looking for an engineer who wants to own their work from start to finish.

In this role you'll take problems from idea to production: building the data, training the models, evaluating them, deploying them and keeping them running. The work spans traditional ML and large language models, and what you build will be used directly by the people making decisions across the business.

• Owning ML systems end to end, from scoping the problem through training, deployment and ongoing maintenance
• Building production LLM pipelines that process large volumes of unstructured text and turn it into clean, structured data
• Training and fine-tuning models, including open-source LLMs where they're the right fit
• Using large models to generate labels and training data, then distilling that into smaller, faster models for production
• Deploying and tuning models on in-house GPU infrastructure, balancing throughput, cost and reliability
• Designing ways to measure model and pipeline quality, including where no ground truth exists
• Building and monitoring data pipelines over large, messy datasets, with care taken to keep historical data accurate
• Working day to day with engineers and researchers, and seeing your work directly shape decisions across the business

• Degree in Computer Science, Math, Statistics, Engineering or a related field
• 5-7+ years building ML systems end to end, from training through production, with continued ownership after launch
• Has led the design of at least one ML system that went to production, including choosing the approach and defining how success was measured
• A solid foundation in machine learning, with hands-on experience training classification, ranking, embedding or tree-based models, not only working with LLMs
• Strong Python and production-quality code
• Experience working with large, imperfect datasets using distributed data tools
• Comfort in Linux environments and with standard tooling like Docker and Kubernetes
• Clear communication and a practical, get-it-right approach
• Open to ML Engineers, Software Engineers (ML) and Applied Scientists who have built, trained and shipped their own models into production, and kept them running after launch

• Experience with LLM serving or multi-GPU deployments
• Vector search, retrieval systems or A/B testing
• Exposure to financial data
• Research background a plus

Machine Learning Engineer, Applied Scientist, Software Engineer (ML), Python, PyTorch, Large Language Models, LLM Inference, Fine-Tuning, Model Serving, Recommendation Systems, Production ML, Quantitative Finance, ML Infrastructure, Search & Ranking, Distributed Systems