Principal Machine Learning Engineer

At Datatonic, we are Google Cloud's premier partner in AI, driving transformation for world-class businesses. We push the boundaries of technology with expertise in machine learning, data engineering, and analytics on Google Cloud Platform. By partnering with us, clients future-proof their operations, unlock actionable insights, and stay ahead of the curve in a rapidly evolving world.

As a Principal Machine Learning Engineer (equivalent to a Lead Engineer in many organisations), you’ll be a visionary leader, driving technical excellence and innovation. You’ll not only engineer beautiful code in Python but also set the standard for high-quality engineering and best practices across production software and rapid prototypes.

This is a hands-on technical role with significant leadership responsibilities. You will lead projects, mentor junior and senior engineers, and actively drive client discussions. Your responsibilities will involve building trusted relationships with prospects, defining strategic approaches to use machine learning to solve complex problems, overseeing project scoping, and ensuring the successful, high-impact delivery of these engagements. You will be a key voice in shaping our technical direction and fostering a culture of innovation.

To be successful, you will need strong ML & Data Science fundamentals and will know the right tools and approach for each ML use case. You’ll be comfortable with model optimisation and deployment tools and practices. Furthermore, you will also need excellent communication and consulting skills, with the desire to meet real business needs and deliver innovative solutions using AI & Cloud.

• Translating Requirements: Interpret ambiguous and complex requirements, translating them into clear technical specifications and leading the development of innovative models to solve real-world, high-impact problems.

• Data Science: Lead and oversee ML experiments, driving the selection of appropriate programming languages and machine learning libraries, and establishing best practices for experimental design and analysis.

• GenAI: Pioneer the application of generative AI to develop groundbreaking and innovative solutions, setting technical direction and strategy.

• Optimisation: Architect and implement advanced optimisation strategies for machine learning solutions, ensuring peak performance, scalability, and cost-efficiency across large-scale systems.

• Custom Code: Design and implement highly tailored, complex machine learning code to meet unique and challenging business needs, often involving novel approaches.

• Data Engineering: Own and define the strategy for efficient data flow between complex databases and distributed backend systems, ensuring data integrity and accessibility for ML initiatives.

• MLOps: Establish and champion MLOps best practices, leading the automation of ML workflows, and driving advancements in testing, reproducibility, and robust feature/metadata storage solutions.

• ML Architecture Design: Lead the design and evolution of sophisticated machine learning architectures, leveraging advanced Google Cloud tools and services to build resilient and scalable platforms.

• Engineering Software for Production: Drive the development and deployment of production-grade software for machine learning and data-driven solutions, ensuring high standards of code quality, reliability, and maintainability.

• Experience: 5+ years of progressive experience as a Machine Learning Engineer, with a significant portion in a leadership or consulting capacity, demonstrating a proven ability to lead complex projects and teams. This includes experience in technical leadership, guiding architectural decisions, and potentially people leadership or mentorship.

• Programming Skills: Proficiency in Python as a backend language, capable of delivering production-ready code in well-tested CI/CD pipelines.

• Cloud Expertise: Familiarity with cloud platforms such as Google Cloud, AWS, or Azure.

• Software Engineering: Hands-on experience with foundational software engineering practices.

• Database Proficiency: Strong knowledge of SQL for querying and managing data.

• Scalability: Experience scaling computations using GPUs or distributed computing systems.

• ML Integration: Familiarity with exposing machine learning components through web services or wrappers (e.g., Flask in Python).

• Soft Skills: Exceptional communication, negotiation, and presentation skills to effectively articulate complex technical concepts to diverse audiences, including senior leadership and clients, and to influence strategic decisions.

• Leadership: Demonstrated ability to lead, inspire, and mentor engineering teams, fostering a culture of innovation, collaboration, and continuous improvement.

• Scale-up experience.

• Cloud certifications (Google CDL, AWS Solution Architect, etc.).

We believe in empowering our team to thrive, with benefits including:

• Holiday: 25 days plus bank holidays (obviously!)

• Health Perks: Private health insurance (Vitality Health) and Smart Health Services

• Fitness & Wellbeing: 50% gym membership discounts (Nuffield Health, Virgin Active, Pure Gym).

• Hybrid Model: A WFH allowance to keep you comfortable.

• Learning & Growth: Access to platforms like Udemy to fuel your curiosity.

• Pension: (Auto-enrolment after probation period. 3% employer contributions raising 1% per year of service to a max of 10%)

• Life Insurance: (3 x your base salary!)

• Income Protection: (up to 75% of base salary, up to 2 years)

• Cycle to Work Scheme

• Tech Scheme

Join us to work alongside AI enthusiasts and data experts who are shaping tomorrow. At Datatonic, innovation isn’t just encouraged - it’s embedded in everything we do. If you’re ready to inspire change and deliver value at the forefront of data and AI, we’d love to hear from you!

Are you ready to make an impact?

Apply now and take your career to the next level.