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- Tasks: Design and deploy cutting-edge generative models while optimising performance.
- Company: Innovative tech firm in London focused on deep learning research.
- Benefits: Competitive salary, flexible work environment, and opportunities for professional growth.
- Why this job: Join a team pushing the boundaries of AI technology and make a real impact.
- Qualifications: 3+ years in deep learning with proven experience in model development.
- Other info: Collaborative culture with a focus on rigorous research and innovation.
The predicted salary is between 48000 - 72000 £ per year.
We are looking for a high-caliber Senior Machine Learning Research Engineer to join our client core technical team in London. This is not a role for "prompt engineers" or those who simply wrap APIs. We are looking for someone who has spent the last 3+ years deep in the weeds of model architecture, training loops, and infrastructure.
You will be responsible for designing, training, and deploying state-of-the-art generative models. You should be as comfortable debugging a numerical instability in a distributed training run as you are discussing the latest architectural breakthroughs in transformer or diffusion papers.
Core Responsibilities- Model Development: Design and train large-scale generative models (LLMs, VLMs, or Image/Video Generative Models) from scratch or through advanced fine-tuning.
- Infrastructure Management: Build and maintain the "plumbing" of deep learning—handling streaming datasets, efficient checkpointing, and robust state management.
- Optimization: Profile and optimize model training and inference performance using PyTorch or JAX to maximize hardware utilization.
- Distributed Training: Implement and scale training across large GPU clusters using modern distributed strategies (e.g., FSDP, DeepSpeed).
- Rigorous Research: Conduct experiments with a scientist’s mindset. Document every run, communicate trade-offs (e.g., latency vs. quality), and translate research into production-ready code.
- Proven Track Record: 3+ years of experience building deep learning systems in industry or high-level research, with tangible outputs (shipped models, open-source contributions, or published papers).
- Domain Expertise: Deep hands-on experience with at least one of the following:
- LLMs: Pre-training, SFT, RLHF, or long-context window management.
- Vision/Video: Diffusion models, GANs, or Autoregressive image generation.
Deep Learning Engineer employer: Randstad Digital
Contact Detail:
Randstad Digital Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Deep Learning Engineer
✨Tip Number 1
Network like a pro! Attend meetups, conferences, or online webinars related to deep learning. Engaging with industry professionals can open doors and give us insights into unadvertised job opportunities.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving generative models or distributed training. This gives us tangible proof of what you can do and makes you stand out.
✨Tip Number 3
Prepare for technical interviews by brushing up on your knowledge of model architectures and debugging techniques. We recommend doing mock interviews with peers to get comfortable discussing your thought process.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who take the initiative to connect directly with us.
We think you need these skills to ace Deep Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with deep learning systems and model architecture. We want to see your hands-on work, so include any shipped models or relevant projects that showcase your skills.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about deep learning and how your experience aligns with our needs. Don’t forget to mention specific projects or research that relate to the role.
Showcase Your Technical Skills: We’re looking for someone who’s mastered Python and frameworks like PyTorch or JAX. Be sure to highlight your technical expertise in your application, especially any experience with distributed training strategies or numerical debugging.
Apply Through Our Website: To make sure your application gets the attention it deserves, apply directly through our website. It’s the best way for us to keep track of your application and ensure it reaches the right people!
How to prepare for a job interview at Randstad Digital
✨Know Your Models Inside Out
Make sure you can discuss the intricacies of model architecture and training loops. Brush up on the latest breakthroughs in generative models, especially transformers and diffusion techniques, as these will likely come up during your interview.
✨Showcase Your Hands-On Experience
Prepare to talk about your past projects where you've built or fine-tuned deep learning systems. Be ready to share specific examples of shipped models or contributions to open-source projects that demonstrate your expertise.
✨Demonstrate Your Problem-Solving Skills
Expect to face technical questions that test your debugging skills, particularly around numerical instabilities in distributed training. Practise explaining your thought process clearly, as this will show your analytical capabilities.
✨Familiarise Yourself with Infrastructure Management
Understand the 'plumbing' of deep learning, including how to handle streaming datasets and efficient checkpointing. Being able to discuss your experience with distributed training strategies will set you apart from other candidates.
