Senior Machine Learning Engineer

About the role

AECOM is hiring a Senior Machine Learning Engineer for its AI for Engineering team in Zug. The team builds AI-driven technology that changes how infrastructure and buildings are designed and engineered, reducing waste and CO₂. You will work on applied machine learning problems spanning optimization, simulation and real-world engineering systems, turning ambiguous problems into scalable ML solutions.

Key responsibilities

• Lead the design and development of advanced machine learning models with measurable impact on engineering and infrastructure applications.
• Own the end-to-end modelling lifecycle, from problem formulation and feature strategy through experimentation, validation, deployment and iteration.
• Design robust experimentation frameworks and evaluation methods, and diagnose model failure modes and performance trade-offs.
• Collaborate with MLOps and software engineering teams to productionize models while keeping a focus on quality and performance.
• Partner with product managers, engineers and domain experts to embed AI capabilities into the SaaS platform.
• Provide technical leadership, mentoring other engineers and shaping modelling approaches and standards.

Required profile

• 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.
• Experience owning ML initiatives end-to-end, from problem definition through deployment and iteration.
• Ability to communicate complex modelling decisions and trade-offs to diverse stakeholders.
• Preferred: background in engineering-heavy domains such as mechanical engineering, construction, transportation, HVAC, energy systems or robotics, and experience mentoring or leading ML initiatives.

Required skills

• Strong Python and modern ML frameworks such as PyTorch, TensorFlow and scikit-learn.
• Depth in model design, optimization and evaluation beyond off-the-shelf approaches.
• Preferred: optimization, simulation, scientific machine learning, physics-informed learning, digital twins, time-series forecasting, computer vision or sensor data.

Hiring process: 25-minute screening call, a take-home challenge, a combined technical and cultural interview, a 1-hour whiteboard session with two engineers, and a 30-minute meeting with leadership. New hires must attend an in-person Day 1 onboarding at an AECOM office. Benefits depend on employment status and may include medical, dental, vision, life, paid time off, flexible work options, a retirement savings plan and an employee stock purchase plan.