Founding Applied AI Engineer (lead Personalization & Intelligence)

Our client, an early-stage AI startup, are hiring a Founding Applied AI Engineer to join their team in New York. The successful candidate will design and build the intelligence layer that powers the company’s personalisation, recommendation systems and external APIs, including transforming behavioural data into meaningful, actionable understanding of users. Responsibilities Partner with founders to identify which user signals (taste, behaviour, intent, identity) drive value. Translate ambiguous product questions into measurable modelling problems. Define what “user understanding” means in a data-driven system. Design and implement recommendation systems including collaborative filtering, matrix factorisation, embedding models and two-tower architectures. Build cross-domain recommendation systems across media and consumption types. Develop scalable systems that convert behavioural signals into actionable user representations. Create trait inference pipelines and behavioural feature systems. Design reusable user feature abstractions for downstream products and APIs. Design and run A/B tests, bandit systems and offline evaluation frameworks. Define what is stored, inferred and exposed via context APIs, and help shape how external systems consume user context safely and effectively. Skillset Strong foundation in machine learning, particularly statistical modelling and feature engineering. Proven experience building production recommendation systems (e.g. collaborative filtering, matrix factorisation, embeddings). Experience working with large-scale behavioural or interaction datasets (ads, media, e-commerce or consumer platforms). Strong Python skills, with comfort in research-style environments such as Jupyter notebooks and experimentation codebases. Deep understanding of classical ML and recommender systems, with pre-LLM or hybrid systems experience strongly preferred. Experience designing and running A/B tests, bandit systems or other online experimentation frameworks. Ability to evaluate models using rigorous statistical methods and sound experimental design. Familiarity with modern ML frameworks such as PyTorch, JAX or equivalent tools. Strong product intuition, with a focus on user impact over purely model-centric metrics. Ability to operate in ambiguous problem spaces and translate technical outputs into product and business decisions. Benefits Salary: $180k – $250k DOE. #J-18808-Ljbffr Alldus