Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Design and optimise machine learning models for user personalisation and data processing.
- Company: Join a forward-thinking tech company at the forefront of AI innovation.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Why this job: Make an impact by developing cutting-edge ML solutions that enhance user experiences.
- Qualifications: Proficiency in Python, TensorFlow, and experience with GCP services required.
- Other info: Collaborative environment with a focus on research and continuous learning.
The predicted salary is between 36000 - 60000 £ per year.
What you’ll be doing
- Model Development: Design, train, and optimise machine learning models for user personalisation, including recommendation systems, ranking models, user segmentation, and content understanding, with a strong focus on TensorFlow-based development.
- Data Pipeline Engineering: Build and maintain scalable data pipelines to support feature engineering and model training across large structured and unstructured datasets, leveraging cloud-native tooling.
- Production Deployment: Deploy, monitor, and maintain ML models in production environments, including cloud-based model serving on GCP. Ensure high availability, strong performance, and continuous model relevance.
- Experimentation: Lead A/B testing and offline experimentation to evaluate model performance and guide ongoing improvement.
- Cross-Functional Collaboration: Work closely with engineering, product, data, and research teams to ensure ML solutions align with product and business goals.
- Research & Innovation: Stay informed on advances in machine learning, deep learning, and personalisation, and evaluate their integration into existing systems.
What you’ll bring
- End-to-end experience across the ML lifecycle: model development, training, deployment, monitoring, and continuous maintenance.
- Strong proficiency in Python and ML frameworks, with expertise in TensorFlow (and experience with PyTorch).
- Experience with GCP machine learning and data services (e.g., Vertex AI, Dataflow, BigQuery, AI Platform, Pub/Sub).
- Hands-on experience with ML training frameworks such as TFX or Kubeflow Pipelines, and model-serving technologies like TensorFlow Serving, Triton, or TorchServe.
- Background working with large-scale batch and real-time data processing systems.
- Strong understanding of recommender systems, ranking models, and personalisation algorithms.
- Familiarity with Generative AI and its use in production environments.
- Strong communication skills and analytical problem-solving abilities.
Machine Learning Engineer in Twickenham employer: Arrows
Contact Detail:
Arrows Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer in Twickenham
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with other Machine Learning Engineers. 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 involving TensorFlow and GCP. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for technical interviews by brushing up on your ML concepts and coding skills. Practice common algorithms and be ready to discuss your past projects in detail. We want you to shine when it comes to demonstrating your expertise!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Machine Learning Engineer in Twickenham
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your experience with TensorFlow, GCP, and any relevant projects that showcase your skills in model development and data pipeline engineering.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about machine learning and how your background aligns with our goals at StudySmarter. Don’t forget to mention any innovative projects you've worked on!
Showcase Your Projects: If you’ve got any personal or professional projects related to ML, make sure to include them. We love seeing practical applications of your skills, especially if they involve recommendation systems or A/B testing.
Apply Through Our Website: We encourage you to apply through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to keep track of your application status directly!
How to prepare for a job interview at Arrows
✨Know Your Models Inside Out
Make sure you can talk confidently about the machine learning models you've developed. Be ready to discuss your experience with TensorFlow and any other frameworks you've used, like PyTorch. Prepare examples of how you've optimised models for user personalisation, as this will show your practical knowledge.
✨Show Off Your Data Pipeline Skills
Since data pipeline engineering is key for this role, brush up on your experience with building and maintaining scalable data pipelines. Be prepared to explain how you've leveraged cloud-native tools, especially on GCP, to support feature engineering and model training. Real-world examples will make your answers stand out!
✨Demonstrate Your Deployment Know-How
Talk about your experience in deploying ML models in production environments. Highlight any specific tools or technologies you've used for model serving, like TensorFlow Serving or Triton. Discuss how you ensure high availability and performance, as this shows you're ready for the challenges of a live environment.
✨Collaborate and Communicate
This role requires cross-functional collaboration, so be ready to share examples of how you've worked with engineering, product, and research teams. Strong communication skills are essential, so practice articulating your thoughts clearly and concisely. This will help demonstrate that you can effectively align ML solutions with business goals.
