Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Develop and optimise machine learning models for games and marketing data.
- Company: Kwalee creates fun casual games and values creativity and collaboration.
- Benefits: Enjoy comprehensive medical cover, 25 days holiday, snacks, gym access, and more!
- Why this job: Join a vibrant team where your work impacts over 1 billion players globally.
- Qualifications: Experience in deploying ML models and proficiency in Python and cloud-based tools required.
- Other info: Dog-friendly office with seasonal events and a strong commitment to diversity and inclusion.
The predicted salary is between 36000 - 60000 £ per year.
At Kwalee, we foster an environment where creativity and collaboration come together. Specialising in both the development and publishing of casual and hybrid casual games, we also bring our creative touch to publishing PC & Console titles, ensuring a diverse and exciting gaming portfolio.
By joining our talented team in Bangalore or Leamington Spa, you’ll be part of a space where ideas flow freely, innovation flourishes, and team spirit stays strong. We’ve found that when we’re all together, great things happen. With over 1 billion players already reached, your work will contribute to our shared mission of making the most fun games for the world\’s players.
Join the team!
As we continue to grow, we are now looking for a Machine Learning Engineer to join our Data & Engineering team.
As a Machine Learning Engineer, you will be responsible for developing and optimising machine learning models to extract value from our games, players, and marketing data. You will work closely with the data science, marketing, and game development teams to build scalable data-driven solutions. Together, you will enhance automated decision-making, improve complex systems, and drive data experimentation to uncover new insights.
This is a permanent role based in our Leamington Spa studio, where our daily in-office collaboration fuels creativity and innovation.
Responsibilities
Develop and improve machine learning models by researching, experimenting, and testing new techniques.
- Extract and process data from various sources to enhance predictive accuracy.
- Work with data scientists, marketing, and game development teams to build valuable data tools.
- Optimise existing models by testing new architectures and strategies.
- Design and implement experiments to understand player behaviour and market trends.
- Collaborate with software engineers to deploy models in scalable products.
Requirements
A few years of industry experience deploying production machine learning models.
- Strong background in training and deploying ML models in real-world applications.
- Experience in marketing-related domains or similar data-driven industries.
- Proficiency in cloud-based ML tools and platforms such as AWS SageMaker, Google Vertex AI, Fabric, or MLOps frameworks.
- Hands-on experience with Python, TensorFlow, PyTorch, or similar ML frameworks.
Why Kwalee?
We believe in more than just a job—we’re committed to helping you thrive with a fantastic work-life balance and a range of great benefits! You’ll enjoy comprehensive medical cover, including dental and optical care, life assurance, and a solid pension plan. Plus, you’ll have 25 days of holiday to recharge, along with unlimited snacks and drinks to keep you energised throughout the day.
Enjoy the benefits of an on-site gym, free parking, and convenient electric charging stations. We provide state-of-the-art equipment and plenty of career growth opportunities to set you up for success. And to keep things lively, we host seasonal events, regular townhalls, and share exclusive Kwalee merch. With our dog-friendly policy and a vibrant office atmosphere, there’s always something fun going on!
Our Commitment to Diversity & Inclusion
At Kwalee, we take pride in being an equal opportunity employer, where we believe that diversity and inclusion are essential to driving creativity. We are committed to creating a safe, welcoming, and supportive environment where everyone can thrive.
Our culture is built on celebrating the diverse voices of our team members, fuelling innovation and strengthening our connection to our players. We are dedicated to advancing equity, diversity, and inclusion across all areas of life, including age, disability, gender identity, marriage and civil partnership, pregnancy and maternity, race, religion or belief, sex, and sexual orientation.
We recognise the importance of self-development, career progression, and well-being in retaining our talented team. At Kwalee, we celebrate individuality and encourage everyone to bring their authentic selves to work. #J-18808-Ljbffr
Machine Learning Engineer employer: Gamigion
Contact Detail:
Gamigion Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Familiarise yourself with the specific machine learning tools and frameworks mentioned in the job description, such as TensorFlow and PyTorch. Having hands-on experience with these technologies will not only boost your confidence but also demonstrate your readiness to hit the ground running.
✨Tip Number 2
Engage with the gaming community and stay updated on the latest trends in machine learning applications within the gaming industry. This knowledge can help you during interviews, showcasing your passion for both machine learning and gaming.
✨Tip Number 3
Network with current or former employees of Kwalee through platforms like LinkedIn. They can provide valuable insights into the company culture and the specific skills that are highly valued, which can give you an edge in your application.
✨Tip Number 4
Prepare to discuss your previous projects involving machine learning, especially those related to marketing or gaming. Be ready to explain your thought process, the challenges you faced, and how you overcame them, as this will highlight your problem-solving skills and practical experience.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Understand the Role: Before applying, make sure you fully understand the responsibilities and requirements of the Machine Learning Engineer position at Kwalee. Tailor your application to highlight relevant experience and skills that align with their needs.
Highlight Relevant Experience: In your CV and cover letter, emphasise your experience in deploying machine learning models, particularly in gaming or marketing-related domains. Use specific examples to demonstrate your expertise with tools like Python, TensorFlow, or cloud-based ML platforms.
Showcase Collaboration Skills: Kwalee values teamwork and collaboration. Make sure to mention any past experiences where you worked closely with data scientists, marketing teams, or game developers. Highlight how your contributions led to successful outcomes.
Craft a Compelling Cover Letter: Write a personalised cover letter that reflects your passion for gaming and machine learning. Discuss why you want to join Kwalee specifically and how you can contribute to their mission of creating fun games for players worldwide.
How to prepare for a job interview at Gamigion
✨Showcase Your Technical Skills
Be prepared to discuss your experience with machine learning frameworks like TensorFlow or PyTorch. Bring examples of projects where you've deployed models in real-world applications, as this will demonstrate your hands-on expertise.
✨Understand the Gaming Industry
Familiarise yourself with the gaming landscape and how machine learning can enhance player experiences. Research Kwalee's games and think about how data-driven solutions could improve their offerings.
✨Collaborative Mindset
Since the role involves working closely with various teams, emphasise your ability to collaborate effectively. Share examples of past teamwork experiences, especially in cross-functional settings, to highlight your communication skills.
✨Prepare for Problem-Solving Questions
Expect technical questions that assess your problem-solving abilities. Practice explaining your thought process when tackling complex issues, particularly those related to optimising machine learning models or extracting insights from data.
