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For Companies At a Glance
- Tasks: Design and optimise machine learning models for user personalisation and build scalable data pipelines.
- Company: Join a forward-thinking tech company focused on innovation and collaboration.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Why this job: Make an impact in the exciting field of machine learning and personalisation.
- Qualifications: Experience in Python, TensorFlow, and end-to-end ML lifecycle.
- Other info: Dynamic team environment with a focus on research and innovation.
The predicted salary is between 36000 - 60000 ÂŁ per year.
Overview
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.
What You’ll Be Doing
- 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 - Arrows in Twickenham employer: Jobster
Contact Detail:
Jobster Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer - Arrows 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 will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and ML frameworks. Practice coding challenges and be ready to discuss your past projects in detail. We want to see how you think and solve problems!
✨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 - Arrows in Twickenham
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with machine learning models, especially in TensorFlow. We want to see how your skills align with our focus on user personalisation and data pipeline engineering.
Showcase Your Projects: Include any relevant projects you've worked on, particularly those involving recommendation systems or cloud-native tooling. This gives us a glimpse into your hands-on experience and problem-solving abilities.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about machine learning and how you can contribute to our team. Mention specific technologies like GCP and your familiarity with A/B testing to stand out.
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 from our team!
How to prepare for a job interview at Jobster
✨Know Your Models
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 using TensorFlow. Prepare examples of recommendation systems or user segmentation projects you've worked on.
✨Data Pipeline Mastery
Brush up on your knowledge of data pipelines. Be prepared to talk about how you've built and maintained scalable data pipelines, particularly in cloud environments like GCP. Highlight any specific tools you've used, such as Dataflow or BigQuery, to show your hands-on experience.
✨Experimentation Insights
Understand the importance of A/B testing and offline experimentation in model evaluation. Be ready to share your experiences leading such tests and how they influenced model improvements. This shows your analytical skills and commitment to continuous improvement.
✨Collaboration is Key
Since this role involves cross-functional collaboration, think of examples where you've worked closely with engineering, product, or research teams. Emphasise your communication skills and how you ensure that ML solutions align with broader business goals.
