Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Design and deliver cutting-edge ML systems for a leading gaming platform.
- Company: Join a dynamic digital gaming and gambling company in London.
- Benefits: Competitive salary up to £95,000, hybrid work, and career growth.
- Why this job: Make a real impact with applied AI and data-driven solutions.
- Qualifications: 3+ years of ML engineering experience and strong Python skills.
- Other info: Collaborative environment with opportunities for innovation and learning.
The predicted salary is between 78000 - 112000 £ per year.
We are seeking a Senior Machine Learning Engineer to design and deliver production-grade ML systems for a leading digital gaming and gambling platform. This is a hands-on role combining data science expertise with engineering skills - you'll build models, optimise algorithms, and deploy solutions at scale to enhance customer engagement and decisioning.
You’ll work closely with Data Scientists to translate prototypes into robust applications, ensuring performance, governance, and reliability. If you’re passionate about applied AI, data-driven problem solving, and building ML systems that deliver measurable impact, this is the role for you.
Role and Responsibilities- Data science & modelling: Develop, validate, and optimise predictive models using advanced ML algorithms (e.g., gradient boosting, logistic regression, ensemble methods).
- End-to-end ML engineering: Deploy models as APIs, batch jobs, and streaming services; implement CI/CD, monitoring, and rollback strategies.
- Feature engineering & pipelines: Build scalable data workflows and feature stores for ML applications.
- Infrastructure & tooling: Containerise applications with Docker, orchestrate with Kubernetes, and deploy securely in AWS.
- Model governance: Apply best practices for evaluation, drift monitoring, and compliance.
- Collaboration: Partner with Data Scientists and business stakeholders to translate insights into production-ready solutions.
- Master's degree in a STEM or quantitative discipline (PhD nice to have).
- 3+ years of industrial ML engineering experience (not purely academic; not focused on Generative AI).
- Strong data science fundamentals: supervised learning, evaluation metrics, feature engineering, and experimentation.
- Production-grade Python proficiency and ability to write clean, maintainable code.
- Comfortable with complex SQL queries.
- Hands-on experience with AWS (ECR/ECS/EKS, Lambda, S3, IAM, CloudWatch), ideally AWS-certified.
- Experience with Docker and Kubernetes in production environments.
- Degree (BSc/MSc) in a STEM or quantitative discipline; PhD desirable.
- Strong communication skills and ability to explain technical concepts clearly.
Apply now with your most up-to-date CV and a short note highlighting your experience with Python, SQL, AWS, Docker, Kubernetes, and data science projects.
Please be aware this advert will remain open until the vacancy has been filled. Interviews will take place throughout this period, therefore we encourage you to apply early to avoid disappointment.
Tate is acting as an Employment Business in relation to this vacancy. Tate is committed to promoting equal opportunities. To ensure that every candidate has the best experience with us, we encourage you to let us know if there are any adjustments we can make during the application or interview process. Your comfort and accessibility are our priority, and we are here to support you every step of the way. Additionally, we value and respect your individuality, and we invite you to share your preferred pronouns in your application.
Senior Machine Learning Engineer employer: Tate
Contact Detail:
Tate Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to your connections in the industry, attend meetups, and engage in online forums. 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 ML projects, especially those that highlight your experience with Python, SQL, AWS, Docker, and Kubernetes. This will give potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice explaining complex concepts clearly, as communication is key in this role. Mock interviews can be super helpful!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Senior Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Senior Machine Learning Engineer role. Highlight your experience with Python, SQL, AWS, Docker, and Kubernetes, as these are key skills we're looking for. A personalised CV shows us you're genuinely interested in the position!
Craft a Compelling Note: Alongside your CV, include a short note that showcases your relevant experience. This is your chance to shine! Tell us about specific projects where you've applied your data science skills and how they made an impact.
Showcase Your Collaboration Skills: Since this role involves working closely with Data Scientists and stakeholders, make sure to mention any collaborative projects you've been part of. We love seeing how you can translate insights into production-ready solutions!
Apply Early!: Don't wait until the last minute to apply. Our advert will remain open until the vacancy is filled, but we encourage you to get your application in early. This way, you won't miss out on the opportunity to join our awesome team!
How to prepare for a job interview at Tate
✨Know Your ML Algorithms
Brush up on your knowledge of advanced machine learning algorithms like gradient boosting and ensemble methods. Be ready to discuss how you've applied these in real-world scenarios, as this will show your hands-on experience and understanding of the subject.
✨Showcase Your Engineering Skills
Prepare to talk about your experience with deploying models as APIs and using CI/CD practices. Highlight any projects where you’ve containerised applications with Docker or orchestrated them with Kubernetes, as this is crucial for the role.
✨Communicate Clearly
Practice explaining complex technical concepts in simple terms. You’ll need to collaborate with Data Scientists and business stakeholders, so being able to convey your ideas clearly will set you apart from other candidates.
✨Demonstrate Your Problem-Solving Skills
Think of specific examples where you've tackled data-driven problems. Be prepared to discuss how you approached feature engineering and built scalable data workflows, as this will showcase your ability to deliver impactful solutions.
