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- Tasks: Develop RL policies for intelligent robots, bridging simulation and real-world applications.
- Company: High-profile robotics organisation in central London with a collaborative culture.
- Benefits: Competitive salary, fantastic benefits, and cutting-edge hardware.
- Why this job: Join a pioneering team and shape the future of robotics and AI.
- Qualifications: 5+ years in deep learning, 3+ years in commercial AI solutions, and RL expertise.
- Other info: Dynamic environment with opportunities for impactful work and career growth.
The predicted salary is between 36000 - 60000 £ per year.
A high-profile robotics organization is urgently seeking a high-caliber RL Engineer (Manipulation) to join their London-based R&D team. This role is pivotal in bridging the gap between simulation and real-world application, focusing on the development of language-vision conditioned policies for next-generation intelligent robotic platforms.
As a RL Engineer you will have the following responsibilities:
- Sim-to-Real Transfer: Developing manipulation tasks in simulators (Isaac Sim/MuJoCo) and successfully deploying trained policies onto physical hardware.
- VLA Policy Development: Training Vision-Language-Action models via RL to enable robots to execute actions based on visual and linguistic context.
- Trajectory Scaling: Collaborating with teleops teams to transform human trajectories into robust robotic skills through behaviour cloning.
- High-Performance Engineering: Designing and profiling research-grade PyTorch/JAX code to support large-scale, distributed RL infrastructure.
Essential Skills Needed:
- Deep Learning Mastery: 5+ years building and shipping models, with deep hands-on expertise in LLMs, VLMs, or generative architectures.
- Industry Experience: 3+ years of commercial experience delivering production-grade AI solutions.
- RL Expert: A proven track record of solving complex, real-world problems using Deep Reinforcement Learning.
- Technical Rigour: Mastery of Python and PyTorch/JAX, including the ability to profile performance and debug complex numerical stability issues.
- Robotics Foundation: Practical experience with simulators (Isaac Sim/MuJoCo) and a deep understanding of sim-to-real bottlenecks.
If you are looking to join a high-profile robotics organization and make a tangible impact on the future of LLMs and Embodied AI, this is the ideal opportunity for you.
Reinforcement Learning (RL) control Engineer in City of London employer: Randstad Digital
Contact Detail:
Randstad Digital Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Reinforcement Learning (RL) control Engineer in City of London
✨Tip Number 1
Network like a pro! Reach out to folks in the robotics and AI community, especially those who work with RL. Attend meetups or webinars, and don’t be shy about asking for informational interviews. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving RL, deep learning, and robotics. Use GitHub to share your code and document your thought process. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and PyTorch/JAX skills. Practice coding challenges related to RL and robotics. Mock interviews with friends or using online platforms can help you get comfortable with the format and types of questions you might face.
✨Tip Number 4
Don’t just apply anywhere; focus on companies that excite you! Check out our website for roles that match your skills and interests. Tailor your approach to each company, showing them why you’re the perfect fit for their team. Let’s land that dream job together!
We think you need these skills to ace Reinforcement Learning (RL) control Engineer in City of London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with deep learning and reinforcement learning. We want to see how your skills align with the role, so don’t be shy about showcasing your projects and achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about robotics and how your background makes you the perfect fit for our team. Keep it engaging and relevant to the job description.
Showcase Your Technical Skills: We’re looking for someone with solid expertise in Python and PyTorch/JAX. Be sure to mention specific projects where you’ve used these technologies, especially in relation to sim-to-real transfer or RL policy development.
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 Stuff
Make sure you brush up on your deep learning and reinforcement learning concepts. Be ready to discuss your experience with LLMs, VLMs, and generative architectures. They’ll want to see that you can not only talk the talk but also walk the walk when it comes to real-world applications.
✨Showcase Your Projects
Prepare to discuss specific projects where you've developed manipulation tasks in simulators like Isaac Sim or MuJoCo. Highlight any challenges you faced with sim-to-real transfer and how you overcame them. Real examples will make your experience more tangible and impressive.
✨Get Technical
Be ready to dive into the technical details of your work. They’ll likely ask about your proficiency in Python and PyTorch/JAX, so have some examples of code or projects handy. Discuss how you’ve tackled performance profiling and debugging issues in your previous roles.
✨Collaborate and Communicate
Since this role involves working closely with teleops teams, be prepared to discuss your collaboration skills. Share examples of how you’ve worked in a team to transform human trajectories into robotic skills. Good communication is key, so show them you can articulate complex ideas clearly.
