Head of Machine Learning

My client, a top-tier systematic hedge fund is hiring a Head of AI & Machine Learning. This is a hands‑on founding role to design and scale the ML backbone of the fund — from automated feature engineering to large‑scale model training — where AI is the core engine of alpha, not a support function.

• Prior hedge fund experience (systematic or quant environment)
• Proven ownership of production ML systems, not just research
• Ability to build platforms that scale across researchers and asset classes

As Head of AI for automated feature engineering & model scaling, you will design, build, and lead the machine learning foundation of this fund.

Your work will directly shape the research infrastructure that fuels all systematic alpha generation — from automated signal discovery to portfolio‑level decision intelligence.

You will collaborate closely with Quant Research, Data Science, and Engineering to embed cutting‑edge ML into every stage of the trading process.

• Design and deployment of a machine‑learning platform for automated feature engineering and large‑scale model training.
• Develop scalable frameworks for signal discovery, data enrichment, and model optimization across multiple asset classes.
• Apply advanced ML techniques (including LLMs, self‑supervised learning, and representation learning) to uncover novel alpha sources from structured and unstructured data.
• Partner with quantitative researchers to translate research insights into production‑grade models and trading strategies.

• PhD or advanced degree in Computer Science, Machine Learning, Applied Math, or related field.
• 8–15 years of experience in applied AI/ML — ideally within finance, large‑scale tech, or advanced quantitative research.
• Proven track record in automated feature engineering, signal modeling, and large‑scale ML deployment.
• Hands‑on expertise with LLMs, graph‑based learning, or self‑supervised learning for unstructured data.
• Strong programming background (Python, C++, or similar) and familiarity with distributed training environments (PyTorch, Tensor Flow, JAX, Ray, etc.).