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For Companies At a Glance
- Tasks: Lead the development of cutting-edge AI models and automate data pipelines.
- Company: Join a leading tech recruitment firm focused on IT and Engineering.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Why this job: Shape the future of AI with innovative projects and advanced technologies.
- Qualifications: 3+ years in deep learning with expertise in VLMs or generative models.
- Other info: Dynamic team environment with a focus on collaboration and innovation.
The predicted salary is between 36000 - 60000 £ per year.
We are seeking a Senior Deep Learning Engineer to build the future of Embodied AI. This is a role for those who work "in the weeds" of model architecture and training loops, not API wrappers or prompt engineers.
The Mission
- Architect Behavior: Own the development of motor policies using Behavior Cloning and RL.
- Scale VLA Research: Lead pre/post-training on our Vision-Language-Action (VLA) stack.
- Engineered Data: Build automated pipelines to ingest teleop logs/synthetic data, apply weak-supervision, and curate high-quality datasets.
- Failure Analysis: Systematically identify and solve failure modes through retraining loops.
Technical Requirements
- Experience: 3+ years in deep learning (shipped models, research papers, or major OSS).
- Domain Depth: Hands-on expertise in VLMs, Diffusion/Generative Video, or LLM Pre-training.
- Frameworks: Mastery of Python and PyTorch or JAX.
- Systems: Experience with distributed training and complex numerical debugging.
Senior Deep Learning Engineer in City of London employer: Randstad Technologies Recruitment
Contact Detail:
Randstad Technologies Recruitment Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Deep Learning Engineer in City of London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your deep learning projects, especially those involving VLA or generative models. This will give you an edge and demonstrate your hands-on expertise.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and PyTorch skills. Practice coding challenges and be ready to discuss your past projects in detail, especially any failure analysis you've conducted.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who take the initiative to engage directly with us.
We think you need these skills to ace Senior Deep Learning Engineer in City of London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in deep learning and showcases any relevant projects you've worked on. We want to see how your skills align with the role, so don’t hold back on those details!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about embodied AI and how your background makes you the perfect fit for our team. Keep it engaging and personal.
Showcase Your Technical Skills: Since we're looking for someone with hands-on expertise in VLMs or similar areas, make sure to mention specific frameworks and tools you've mastered. We love seeing practical examples of your work!
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’s super easy!
How to prepare for a job interview at Randstad Technologies Recruitment
✨Know Your Models Inside Out
As a Senior Deep Learning Engineer, you’ll need to demonstrate a deep understanding of model architecture. Brush up on your knowledge of Behavior Cloning and RL, and be ready to discuss specific projects where you've applied these techniques.
✨Showcase Your Data Engineering Skills
Be prepared to talk about how you've built automated pipelines for data ingestion and curation. Highlight any experience with teleop logs or synthetic data, and discuss how you’ve applied weak-supervision in past projects.
✨Prepare for Technical Challenges
Expect technical questions that test your mastery of Python and frameworks like PyTorch or JAX. Practise solving complex numerical debugging problems, as this will likely come up during the interview.
✨Discuss Failure Analysis Tactics
Have examples ready that illustrate how you've identified and solved failure modes in your previous work. Discuss your approach to retraining loops and how you ensure high-quality datasets are maintained.
