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- Tasks: Develop cutting-edge features using Machine Learning for dynamic game worlds.
- Company: Join an innovative game studio known for captivating and advanced gaming experiences.
- Benefits: Work in a collaborative environment with opportunities for personal growth and creativity.
- Why this job: Make a real impact in the gaming world while pushing the boundaries of technology.
- Qualifications: Strong software engineering skills and experience with Machine Learning libraries like PyTorch required.
- Other info: Autonomous work style and effective communication are key to success in this role.
The predicted salary is between 36000 - 60000 £ per year.
Company Overview: We are partnering with an innovative and forward-thinking game development studio, known for creating captivating and technically advanced games that engage a global audience. The studio is driven by a passion for crafting meaningful gaming experiences and pushing the boundaries of what's possible. They are now looking for talented engineers to join their team and contribute to exciting, ground breaking projects. If you’re ready to make an impact in the gaming world, this is an incredible opportunity.
Role Overview: As a Software Engineer with this studio, you will play a key role in the development of cutting-edge features that utilize Machine Learning to create dynamic, procedurally generated game worlds. You will collaborate with a dedicated and forward-looking team to design, implement, and iterate on new features, helping to shape the future of their games.
Key Responsibilities:- Lead the development of new and existing features utilizing Machine Learning to generate immersive, procedurally generated worlds within the games.
- Collaborate with a humble, empathetic, and ambitious development team to push the boundaries of innovation in the gaming space.
- Drive the design and iteration of features, incorporating feedback from peers and stakeholders to refine and improve your work.
- Manage your workload autonomously, prioritizing tasks and ensuring timely delivery of high-quality results.
- Communicate effectively within the team, sharing progress, challenges, and insights while being receptive to feedback.
- Strong software engineering skills, with a solid understanding of algorithms, data structures, and software architecture.
- Experience with Machine Learning engineering, particularly with modern libraries such as PyTorch.
- Deep knowledge of generative models, including expertise in diffusion models and Variational Autoencoders (VAEs).
- Proven ability to work autonomously, take ownership of tasks, and drive them to completion.
- Strong communication skills, with the ability to collaborate effectively within a team-oriented environment.
- Able to prioritize tasks and adapt to changing circumstances in a fast-paced, evolving environment.
Machine Learning Engineer employer: Skillsearch
Contact Detail:
Skillsearch Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Familiarise yourself with the latest trends in machine learning, especially in gaming. Understanding how generative models like diffusion models and VAEs are applied in game development can give you an edge during discussions with the team.
✨Tip Number 2
Showcase your collaborative skills by engaging with online communities or forums related to game development and machine learning. This not only helps you network but also demonstrates your passion for teamwork and innovation.
✨Tip Number 3
Prepare to discuss specific projects where you've implemented machine learning solutions. Be ready to explain your thought process, the challenges you faced, and how you overcame them, as this will highlight your problem-solving abilities.
✨Tip Number 4
Practice articulating your ideas clearly and concisely. Since strong communication is key in this role, being able to convey complex concepts in an understandable way will impress the interviewers and show that you're a great fit for their team.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in software engineering and machine learning. Emphasise your skills with algorithms, data structures, and any specific projects involving PyTorch or generative models.
Craft a Compelling Cover Letter: In your cover letter, express your passion for gaming and how your background aligns with the studio's mission. Mention specific experiences where you've successfully implemented machine learning solutions in previous projects.
Showcase Your Projects: If you have any personal or professional projects that demonstrate your expertise in machine learning, especially with generative models, include links or descriptions in your application. This will help illustrate your capabilities.
Highlight Team Collaboration: Since the role involves working within a team, mention instances where you've collaborated effectively with others. Discuss how you’ve communicated progress and incorporated feedback to improve your work.
How to prepare for a job interview at Skillsearch
✨Showcase Your Technical Skills
Be prepared to discuss your experience with algorithms, data structures, and software architecture. Highlight specific projects where you've successfully implemented Machine Learning techniques, especially using libraries like PyTorch.
✨Demonstrate Your Knowledge of Generative Models
Since the role requires expertise in generative models, make sure to explain your understanding of diffusion models and Variational Autoencoders (VAEs). Bring examples of how you've applied these concepts in previous work or projects.
✨Emphasise Collaboration and Communication
The studio values teamwork, so be ready to share experiences where you collaborated effectively with others. Discuss how you handle feedback and iterate on your work based on team input.
✨Prepare for Problem-Solving Questions
Expect to face technical challenges during the interview. Practice solving problems related to Machine Learning and game development, and be ready to explain your thought process clearly and concisely.
