Senior Machine Learning Engineer

In AECOM’s AI Engineering team your code will directly shape the physical world around us. We build AI-driven technology that revolutionises how infrastructure and buildings are designed and engineered; reducing waste, cutting CO₂, and making the built environment more efficient and sustainable. This is where software has measurable, real-world impact. With our AI Engineering team we’ve created a unique setup: a lean, highly technical team with the speed and ownership of a start up, backed by the scale, resources, and domain expertise of one of the world’s leading engineering firms.  There has never been a better time to be at AECOM. We are leading the industry’s AI transformation, and with our people and technology we deliver excellence and innovate with impact. We invite you to bring your bold ideas and big dreams to solve the world’s most complex challenges. We're one global team driven by our common purpose to deliver a better world. Join us. What You’ll Do - Lead the design and development of advanced AI/ML models that deliver measurable impact in the engineering domain - Own the end-to-end modelling lifecycle — from problem formulation and feature strategy to validation and production handoff - Tackle complex, ambiguous problems and translate them into scalable ML solutions - Design robust experimentation frameworks and define evaluation methodologies that ensure real-world performance - Collaborate closely with MLOps to productionize models while focusing on modelling excellence and performance optimization - Partner with product managers, engineers, and domain experts to embed AI capabilities into our SaaS platform Must-Have Qualifications - Proven experience developing and deploying ML models in production settings - Master’s or PhD in Engineering, Computer Science, Applied Mathematics, Data Science, Operations Research, or a related field - Strong expertise in Python and modern ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn) - Demonstrated depth in model design, optimization, and evaluation beyond standard off-the-shelf approaches - Strong intuition for model failure modes, generalization, and real-world performance trade-offs - Proven ability to independently scope, execute, and deliver high-impact ML initiatives - Clear communicator who can explain complex modelling decisions and trade-offs to diverse stakeholders Preferred Skills - Background in optimization, simulation, or physics-informed ML - Experience applying ML in engineering-heavy domains (civil, HVAC, mechanical, energy systems) - Track record of mentoring or technically leading ML initiatives