Senior Machine Learning Engineer

Description

The AI for Engineering team builds AI-driven products that change how infrastructure and buildings are designed and engineered, cutting waste and CO₂ and making the built environment more efficient and sustainable.

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

You will solve applied machine learning problems in optimization, simulation and real engineering systems, taking models out of the lab and into products that address real infrastructure challenges. Many problems are ambiguous and have no defined solution yet.

The company is a global infrastructure consulting firm, a Fortune 500 business with revenue of $16.1 billion in fiscal year 2025. It works in water, environment, energy, transportation and buildings, across advisory, planning, design, engineering, and program and construction management. Over 50,000 planners, designers, engineers, scientists and other professionals work there.

Responsibilities

• Lead the design and building of advanced machine learning models that produce measurable results in engineering and infrastructure.
• Manage the full modelling cycle, from framing the problem and choosing features through experiments, validation, deployment and ongoing iteration.
• Turn complex, unclear problems into ML solutions that scale.
• Build experimentation frameworks and evaluation methods that measure how models perform in the real world.
• Find the causes of model failure, weigh performance trade-offs, and keep improving models once they are live.
• Work with MLOps and software engineers to move models into production without losing focus on modelling quality and performance.
• Work with product managers, engineers and domain experts to add AI features to the SaaS platform.
• Lead technical work across ML initiatives, mentor other engineers, and help set modelling approaches, experimentation standards and technical direction.

Requirements

• Proven experience building and deploying ML models in production.
• Master's or PhD in Engineering, Computer Science, Applied Mathematics, Data Science, Operations Research or a related field.
• Strong Python skills and experience with modern ML frameworks such as PyTorch, TensorFlow or scikit-learn.
• Depth in model design, optimization and evaluation that goes past standard off-the-shelf methods.
• Strong sense of model failure modes, generalization and real-world performance trade-offs.
• Experience owning ML initiatives end to end, from problem definition through deployment and iteration.
• Proven ability to scope, run and deliver high-impact ML work on your own.
• Ability to explain complex modelling decisions and trade-offs clearly to a varied audience.
• 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 optimization, 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 work.

Conditions

Attendance at an in-person Day 1 onboarding at a company office is a condition of employment, as part of the company's fraud-prevention hiring process.

Equal opportunity employer; applicant information is kept confidential under EEO guidelines.

Benefits

• Work on real problems where AI makes a measurable difference.
• Your ideas can become real work on a team that values them.
• Work alongside capable, motivated colleagues in a culture built on trust, ownership and high standards.
• Help make the built environment smarter and more sustainable.
• Benefits may include medical, dental, vision, life, AD&D and disability cover, paid time off, leaves of absence, voluntary benefits, perks, flexible work options, well-being resources, an employee assistance program, business travel insurance, service recognition awards, a retirement savings plan and an employee stock purchase plan.
• Training and development programs to build technical and leadership skills, plus access to cutting-edge technology and a network of experts.