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- Tasks: Design and implement advanced AI algorithms for game testing and mentor junior team members.
- Company: Global leader in gaming and entertainment with an inclusive culture.
- Benefits: Hybrid working model, competitive salary, and opportunities for professional growth.
- Why this job: Combine your passion for gaming with cutting-edge machine learning technology.
- Qualifications: Experience in machine learning and strong data analysis skills.
- Other info: Collaborative environment with a focus on innovation and creativity.
The predicted salary is between 36000 - 60000 £ per year.
A global leader in gaming and entertainment is seeking a Senior Machine Learning Engineer specializing in anomaly detection. This role involves designing and implementing advanced AI algorithms for game testing, collaborating across teams, and mentoring junior members.
Ideal candidates will have experience in machine learning, a strong understanding of data analysis, and a passion for gaming. The position offers a hybrid working model and an inclusive culture.
Senior ML Engineer – Anomaly Detection (Hybrid) employer: PlayStation Network
Contact Detail:
PlayStation Network Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior ML Engineer – Anomaly Detection (Hybrid)
✨Tip Number 1
Network like a pro! Reach out to folks in the gaming and AI space on LinkedIn. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those related to anomaly detection. This will give you an edge over other candidates.
✨Tip Number 3
Prepare for the interview by brushing up on your technical knowledge and soft skills. Be ready to discuss your past experiences and how they relate to the role.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive!
We think you need these skills to ace Senior ML Engineer – Anomaly Detection (Hybrid)
Some tips for your application 🫡
Show Your Passion for Gaming: When you're writing your application, let your love for gaming shine through! Share any relevant experiences or projects that highlight your enthusiasm for the industry. We want to see how your passion aligns with our mission.
Highlight Your ML Expertise: Make sure to showcase your experience in machine learning and anomaly detection. Use specific examples from your past work to demonstrate your skills. We’re looking for someone who can hit the ground running, so don’t hold back!
Collaborative Spirit is Key: Since this role involves working across teams, emphasise your collaborative experiences. Talk about how you've worked with others to solve problems or mentor junior members. We value teamwork, so let us know how you can contribute!
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. Plus, it shows you’re keen to join our team!
How to prepare for a job interview at PlayStation Network
✨Know Your Algorithms
Brush up on your knowledge of advanced AI algorithms, especially those related to anomaly detection. Be ready to discuss how you've implemented these in past projects and the impact they had on game testing.
✨Show Your Passion for Gaming
Since this role is with a gaming company, make sure to express your enthusiasm for gaming. Share your experiences with games that have used machine learning and how you think it can enhance player experience.
✨Collaboration is Key
Prepare examples of how you've successfully collaborated with cross-functional teams. Highlight your communication skills and how you’ve mentored junior members, as teamwork will be crucial in this role.
✨Data Analysis Skills Matter
Be ready to demonstrate your understanding of data analysis techniques. Discuss specific tools or methods you've used to analyse data in previous roles, and how those insights led to actionable outcomes.
