Senior Machine Learning Engineer

Description

Your code will shape the physical world, helping design and engineer infrastructure and buildings with less waste and lower CO₂.

The AI Engineering team has the pace and ownership of a startup, backed by the scale, resources and domain knowledge of a leading engineering firm.

You will apply machine learning to optimization, simulation and real engineering systems, turning models into products that solve infrastructure problems. The problems are often ambiguous and have no defined solution yet.

The company is a Fortune 500 infrastructure firm with $16.1 billion in revenue for fiscal year 2025 and over 50,000 staff, working across water, environment, energy, transportation and buildings.

Responsibilities

• Lead the design of advanced ML models that have measurable effect on engineering and infrastructure work.
• Own the full modelling lifecycle: framing the problem, choosing features, experimenting, validating, deploying and iterating.
• Take on hard, unclear problems and turn them into ML solutions that scale.
• Build experiment setups and evaluation methods that show how models perform in the real world.
• Find out why models fail, weigh performance trade-offs, and keep improving models after release.
• Work with MLOps and software engineers to put models into production without losing modelling quality.
• Work with product managers, engineers and domain experts to add AI features to the SaaS platform.
• Provide technical direction across ML work, mentoring engineers and setting modelling and experimentation standards.

Requirements

• You have built and deployed ML models in production.
• You hold a Master's or PhD in engineering, computer science, applied mathematics, data science, operations research or a related field.
• You know Python and modern ML frameworks such as PyTorch, TensorFlow or scikit-learn well.
• You have deep skill in model design, optimization and evaluation that goes beyond off-the-shelf methods.
• You understand model failure modes, generalization and real-world performance trade-offs.
• You have owned ML initiatives end to end, from problem definition through deployment and iteration.
• You can scope, carry out and deliver high-impact ML work on your own.
• You explain complex modelling choices and trade-offs clearly to varied audiences.
• Preferred: experience applying ML to engineering-heavy or physical-world fields such as mechanical engineering, construction, transportation, HVAC, energy systems or robotics.
• Preferred: background in optimisation, simulation, scientific machine learning, physics-informed learning or digital twins.
• Preferred: experience with time-series forecasting, computer vision, sensor data or other complex engineering datasets.
• Preferred: track record of mentoring or technically leading ML initiatives.

Conditions

You must attend an in-person Day 1 onboarding at a company office as a condition of employment.

Applicant information is kept confidential under EEO guidelines, and the company is a Disability Confident Employer that interviews qualifying applicants with a disability or long-term condition.

Benefits

• Medical, dental, vision, life, AD&D and disability benefits.
• Paid time off and leaves of absence.
• Voluntary benefits, perks, flexible work options and well-being resources.
• Employee assistance programme, business travel insurance and service recognition awards.
• Retirement savings plan and employee stock purchase plan.
• Award-winning training and development programmes to grow your technical and leadership skills.
• Work on real-world problems where AI makes a measurable difference to the built environment.