Senior Machine Learning Engineer

Location: London, England; hybrid

Company

hirify.global builds autonomous AI control and optimisation systems that help cement, steel, glass, and other heavy industrial plants operate more efficiently, stably, and with lower carbon emissions.

What you will do

• Build, test, and deploy machine learning models for autonomous industrial control systems.
• Contribute to technical innovation and problem-solving across the machine learning lifecycle.
• Collaborate with the product team on customer projects, planning and delivering technical work packages.
• Design and implement robust, maintainable, and scalable machine learning systems.
• Build development tooling, tests, and continuous-build practices that improve engineering speed and sustainability.
• Establish engineering best practices and contribute to the wider product development effort.

Requirements

• At least 2 years of experience as a machine learning engineer.
• Theoretical machine learning knowledge and practical experience implementing multiple ML techniques.
• Proficiency in Python and familiarity with ML libraries and tools such as scikit-learn and PyTorch.
• Experience working in a scientific, multidisciplinary environment, particularly across physics, chemistry, materials science, or engineering.
• Experience with time-series modelling and industrial or IoT data.
• Understanding of modern cloud infrastructure for machine learning and experience with AWS, GCP, Azure, or comparable providers.

Nice to have

• Experience with dynamical systems, reinforcement learning, system identification, optimisation, or Bayesian statistics.
• A degree in machine learning, physics, or chemistry.
• Experience working in a fast-paced startup environment with agile processes.
• Strong interest in climate change mitigation and the company mission.

Culture & Benefits

• Flexible working with autonomy to decide how and when to work with the team.
• Equity through company share options.
• 30 days of holiday plus bank holidays.
• Generous pension scheme.
• Engineering culture focused on continuous builds, testing, constructive reviews, honesty, ownership, kindness, and practical impact.

Hiring process

• Introductory call with a talent partner.
• Machine learning fundamentals discussion, technical interview, problem-solving exercise, ML engineering exercise, and ML architecture discussion.
• Behaviours and operating principles interview followed by an informal meeting with the CEO or COO; selected interviews can be remote or onsite.