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- Tasks: Lead innovative machine learning projects and mentor a dynamic team of engineers.
- Company: Join Datatonic, a premier Google Cloud partner driving AI transformation.
- Benefits: Enjoy 25 days holiday, private health insurance, gym discounts, and a hybrid work model.
- Why this job: Shape the future of AI while making a real impact in a collaborative environment.
- Qualifications: 5+ years in machine learning with strong leadership and programming skills.
- Other info: Be part of a culture that values innovation and continuous growth.
The predicted salary is between 54000 - 84000 £ per year.
Shape the Future of AI & Data with Us. 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.
Your Mission: 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.
What You’ll Do:
- 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.
What You’ll Bring:
- 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.
Bonus Points If You Have:
- Scale-up experience.
- Cloud certifications (Google CDL, AWS Solution Architect, etc.).
What’s in It for You? 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
Why Datatonic? 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.
Principal Machine Learning Engineer in City of London employer: Datatonic
Contact Detail:
Datatonic Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Principal Machine Learning Engineer in City of London
✨Tip Number 1
Network like a pro! Reach out to your connections in the industry, attend meetups, and engage in online forums. The more people you know, the better your chances of landing that Principal Machine Learning Engineer role.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your best machine learning projects. Whether it's a GitHub repo or a personal website, let your work speak for itself and demonstrate your expertise in Python and ML.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Practice explaining complex concepts clearly and confidently, as you'll need to communicate effectively with clients and team members alike.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, it shows you're genuinely interested in joining Datatonic and being part of our innovative team.
We think you need these skills to ace Principal Machine Learning Engineer in City of London
Some tips for your application 🫡
Show Your Passion for AI & Data: When writing your application, let your enthusiasm for AI and data shine through! We want to see how you can shape the future with us, so share your experiences and projects that highlight your passion for machine learning and innovation.
Tailor Your Application: Make sure to customise your application to fit the Principal Machine Learning Engineer role. Highlight your leadership experience, technical skills in Python, and any cloud expertise you have. We love seeing how your unique background aligns with our mission!
Be Clear and Concise: While we appreciate detail, clarity is key! Use straightforward language to explain your experiences and achievements. This helps us quickly understand your qualifications and how you can contribute to our team.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates. Plus, it shows you’re keen to join our team!
How to prepare for a job interview at Datatonic
✨Know Your Tech Inside Out
As a Principal Machine Learning Engineer, you’ll need to demonstrate your expertise in Python and machine learning frameworks. Brush up on your coding skills and be ready to discuss your past projects in detail, especially those that involved complex problem-solving and innovative solutions.
✨Showcase Your Leadership Skills
This role requires strong leadership abilities, so prepare examples of how you've mentored teams or led projects. Be ready to discuss your approach to fostering a culture of innovation and collaboration, as well as how you handle client discussions and project scoping.
✨Communicate Clearly and Confidently
Exceptional communication is key for this position. Practice explaining complex technical concepts in simple terms, as you’ll need to articulate your ideas to diverse audiences, including senior leadership and clients. Consider doing mock interviews to refine your presentation skills.
✨Prepare for Scenario-Based Questions
Expect to face scenario-based questions that assess your problem-solving abilities and technical knowledge. Think about how you would approach real-world challenges using machine learning and data engineering, and be prepared to discuss your thought process and decision-making criteria.
