Machine Learning Engineering Lead, Climate X

• Strong experience leading applied ML teams, following a strong hands-on IC career
• Proven track record of shipping ML to production and driving real product impact
• Fluency in Python and modern ML libraries (PyTorch, scikit-learn, etc.)
• Depth in one or more of: GenAI, computer vision, geospatial or time-series ML
• Excellent judgement on trade-offs, risk, and delivery pace
• Strong communication skills—able to partner effectively with Product, Engineering, Science, and Commercial teams
• Start-up mindset—thrives in lean, high-growth teams and rolls up sleeves to own problems end-to-end

Desirable

• We’re looking for an Machine Learning Lead to lead our applied machine-learning function and define how Climate X delivers technical excellence at scale
• This is a hands-on leadership post where you’ll set the ML strategy, steer the technical roadmap, and guide a talented team of five ML engineers (with future head-count growth tied to impact) as they bring models from prototype to production
• You’ll work across generative AI, computer vision, geospatial data, and time-series forecasting, solving real-world problems that drive customer value
• You’ll partner closely with our climate risk Science team to marry ML with physical-modelling approaches. As a member of the leadership team, you’ll shape how we build, scale, and operate—playing a critical role in innovation, delivery, and culture as we grow
• You will also collaborate with Directors to cultivate the systems, habits, and culture that enable Climate X to thrive
• Set direction. Own and continually refine the AI & ML strategy and technical roadmap, collaborating with the Product team to ensure AI&ML adds value to our product roadmap
• Lead & grow the team. Lead, mentor, and grow a high-performing team of ML engineers
• Ship product impact. Drive GenAI development for Carta and future features, taking prototypes to production
• Bridge Science & Engineering. Combine ML with physics-based risk models (flooding, tropical cyclones, wildfires) to deliver grounded, high-impact solutions
• Raise the bar. Establish gold-standard practices for evaluation, deployment, observability, and maintainability in ML model development
• Shape culture. Work shoulder-to-shoulder with Directors to embed the rituals and behaviours that make teams exceptional
• Scan the horizon. Evaluate emerging tools, methods, and regulations to keep us ahead of the curve
• Translate value. Turn complex technical challenges into clear business outcomes for colleagues and customers