Lead Machine Learning Engineer

About the Role

Our client is seeking a highly experienced and motivated Lead Machine Learning Engineer to spearhead their AI initiatives. This is a fully remote position, offering the flexibility to work from anywhere within the UK. You will be responsible for designing, developing, and deploying scalable machine learning solutions that address complex business challenges. This role involves leading a talented team of engineers, driving technical strategy, and ensuring the successful implementation of ML models into production environments. We are looking for a visionary leader who can inspire a team and deliver impactful AI-driven products in a collaborative, remote-first setting.

Key Responsibilities

• Lead the design, development, and deployment of machine learning models and systems.
• Architect scalable and robust ML pipelines for data processing, training, and inference.
• Collaborate with data scientists and software engineers to translate models into production-ready code.
• Mentor and guide a team of machine learning engineers, fostering a culture of technical excellence.
• Define and implement MLOps best practices, including monitoring, testing, and CI/CD for ML systems.
• Evaluate and select appropriate ML technologies and tools for various project needs.

Requirements

• Master's or Ph.D. in Computer Science, Engineering, or a related quantitative field.
• 7+ years of experience in machine learning engineering, with at least 2 years in a leadership role.
• Proficiency in Python and ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Strong experience with cloud platforms (AWS, Azure, or GCP) and MLOps tools.
• Experience building and deploying ML models in production environments at scale.
• Excellent communication and leadership skills, with a proven ability to manage remote teams effectively.

Benefits

• Competitive salary and annual bonus structure.
• Generous stock options package.
• Flexible remote working policy with a dedicated home office stipend.
• Comprehensive private healthcare coverage.
• Professional development budget for conferences, courses, and certifications.