At a Glance
- Tasks: Design and optimise machine learning models for user personalisation and deploy them in production.
- Company: Fractal, a strategic AI partner to Fortune 500 companies with a passion for innovation.
- Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on research and innovation.
- Why this job: Join a team that empowers imagination with intelligence and make a real impact.
- Qualifications: Expertise in machine learning lifecycle and proficiency in Python and ML frameworks.
The predicted salary is between 60000 - 80000 £ per year.
It's fun to work in a company where people truly BELIEVE in what they are doing! We’re committed to bringing passion and customer focus to the business.
12 Month FTC, West London – Hybrid: Onsite 2 days per week
Fractal is a strategic AI partner to Fortune 500 companies. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets. We believe that a true Fractalite empowers imagination with intelligence.
Responsibilities
- Model Development: Design, train, and optimise machine learning models focused on user personalisation, including recommendation engines, ranking algorithms, user segmentation, and content analysis.
- Data Pipeline Engineering: Construct and maintain scalable data pipelines for feature engineering and model training using both structured and unstructured large‑scale datasets.
- Production Deployment: Deploy and supervise ML models in production ensuring high availability, optimal performance, and continued relevance.
- Experimentation: Lead the design and analysis of A/B tests and offline experiments to evaluate model efficacy and support continuous improvement.
- Cross‑Functional Collaboration: Engage with multidisciplinary teams to align machine learning initiatives with business objectives and user needs.
- Research & Innovation: Evaluate emerging research in machine learning, deep learning, and personalisation for potential integration within existing systems.
Qualifications
- Demonstrated expertise in the full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance.
- Proficiency in Python and knowledge of ML libraries/frameworks such as TensorFlow and PyTorch.
- Experience with ML training frameworks (e.g. TFX, Kubeflow Pipelines SDK) and model serving technologies (e.g. TensorFlow Serving, Triton, TorchServe).
- Experience with high‑volume data processing and real‑time streaming architectures.
- Strong understanding of recommendation system design and personalisation algorithms.
- Familiarity with Generative AI and its applications in production settings.
- Good communication and analytical problem‑solving skills.
Good To Have
- Experience working on OTT platforms.
- Experience in Scala.
Senior AI Engineer employer: Fractal
Fractal is an exceptional employer that fosters a vibrant work culture where innovation and collaboration thrive. With a strong commitment to employee growth, we offer opportunities for continuous learning and development in the rapidly evolving field of AI. Located in West London, our hybrid work model promotes a healthy work-life balance while empowering our team to make impactful contributions to Fortune 500 companies.
StudySmarter Expert Advice🤫
We think this is how you could land Senior AI Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with fellow AI enthusiasts. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those related to personalisation and recommendation systems. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Be ready to discuss your experience with Python, TensorFlow, and any relevant ML frameworks. Practice common interview questions to boost your confidence!
✨Tip Number 4
Don’t forget to apply through our website! We love seeing passionate candidates who align with our mission. Plus, it’s a great way to ensure your application gets noticed by the right people.
We think you need these skills to ace Senior AI Engineer
Some tips for your application 🫡
Show Your Passion:When you're writing your application, let your enthusiasm for AI and machine learning shine through! We want to see that you truly believe in what you're doing and how it aligns with our mission at StudySmarter.
Tailor Your CV:Make sure your CV is tailored to the Senior AI Engineer role. Highlight your experience with model development, data pipelines, and any relevant projects you've worked on. We love seeing how your skills match up with what we're looking for!
Be Clear and Concise:Keep your application clear and to the point. Use bullet points where possible to make it easy for us to read. We appreciate a well-structured application that gets straight to the good stuff!
Apply Through Our Website:Don't forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it gives you a chance to explore more about what we do at StudySmarter.
How to prepare for a job interview at Fractal
✨Know Your Models Inside Out
Make sure you can discuss your experience with machine learning models in detail. Be ready to explain how you've designed, trained, and optimised models, especially around user personalisation. Use specific examples from your past work to showcase your expertise.
✨Showcase Your Data Pipeline Skills
Prepare to talk about your experience with data pipeline engineering. Highlight any projects where you've constructed scalable data pipelines for feature engineering and model training. Discuss the types of datasets you've worked with, both structured and unstructured, to demonstrate your versatility.
✨Emphasise Cross-Functional Collaboration
Fractal values teamwork, so be ready to share examples of how you've collaborated with multidisciplinary teams. Discuss how you've aligned machine learning initiatives with business objectives and user needs, showcasing your ability to communicate effectively across different functions.
✨Stay Updated on AI Trends
Research the latest trends in machine learning and personalisation, especially around Generative AI. Be prepared to discuss how emerging research could integrate into existing systems. This shows your passion for the field and your commitment to continuous improvement.
