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- Tasks: Join us to build cutting-edge generative systems for video and audio synthesis.
- Company: Dynamic tech company at the forefront of machine learning innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment with mentorship opportunities and career advancement.
- Why this job: Make a real impact in the exciting field of generative AI.
- Qualifications: Experience in deploying ML systems and proficiency in Python required.
The predicted salary is between 60000 - 80000 £ per year.
We are looking for a Machine Learning Engineer to join our team and help build state-of-the-art generative systems for video and audio synthesis, performance transfer, and visual translation. This role is hands-on and technical, requiring experience in training and deploying deep learning models, a solid engineering mindset, and the ability to work cross-functionally with research, product, and creative teams. As a senior member of the team, you’ll shape core infrastructure, set best practices, communicate with stakeholders, and mentor junior engineers while delivering high-quality ML pipelines in production environments.
Must Haves
- Proven track record of deploying ML systems in production.
- A strong understanding of Machine Learning fundamentals.
- Experience with deep learning frameworks such as PyTorch or TensorFlow.
- Experience with training diffusion, transformer, or generative video models.
- Proficiency in Python.
- Experience building and maintaining scalable infrastructure (AWS, GCP, or custom solutions).
- Familiarity with CI/CD workflows, testing, and development best practices.
- Ability to mentor junior engineers and work independently.
Nice to Have
- Prior experience in generative AI for video, audio, or multimodal content.
- Experience with performance optimisation for ML models and pipelines.
- Background in computer graphics, real-time rendering, or VFX pipelines.
- Open-source contributions or published research in machine learning.
- Entrepreneurial mindset or experience working with startups or fast-paced teams.
About You
- Innovative and solutions driven.
- Embrace challenges.
- Adaptable.
- Calm under pressure.
- Strong communication skills.
Machine Learning Engineer employer: DNEG
Contact Detail:
DNEG Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨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 projects, especially those involving deep learning models or generative AI. 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 ML fundamentals and coding skills. Practice common interview questions and work on real-world problems to demonstrate your expertise in frameworks like PyTorch or TensorFlow.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Plus, it’s a great way to ensure your application gets seen by the right people.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with deploying ML systems and deep learning frameworks like PyTorch or TensorFlow. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Share your passion for machine learning and how you’ve tackled challenges in previous roles. We love seeing candidates who can communicate their innovative ideas clearly.
Showcase Your Projects: If you've worked on generative AI projects or have open-source contributions, make sure to include them! We’re keen on seeing practical examples of your work, especially those that demonstrate your ability to build scalable infrastructure.
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’re considered for the role. Plus, it shows you’re serious about joining our team!
How to prepare for a job interview at DNEG
✨Know Your ML Fundamentals
Brush up on your machine learning fundamentals before the interview. Be ready to discuss concepts like overfitting, model evaluation metrics, and the differences between supervised and unsupervised learning. This will show that you have a solid foundation and can engage in technical discussions.
✨Showcase Your Projects
Prepare to talk about specific projects where you've deployed ML systems in production. Highlight your experience with frameworks like PyTorch or TensorFlow, and be ready to explain the challenges you faced and how you overcame them. Real-world examples will make your skills more tangible.
✨Familiarise with Their Tech Stack
Research the company’s tech stack and be prepared to discuss how your experience aligns with their needs. If they use AWS or GCP for scalable infrastructure, mention your relevant experience and any CI/CD workflows you’ve implemented. This shows you’re proactive and genuinely interested in the role.
✨Demonstrate Your Mentorship Skills
Since this role involves mentoring junior engineers, think of examples where you've guided others or contributed to team growth. Share how you approach mentorship and collaboration, as strong communication skills are key in cross-functional teams.