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- Tasks: Join a team to develop real-world AI systems for robotics, integrating ML and control.
- Company: A pioneering robotics company led by experienced entrepreneurs, focused on embodied AI innovation.
- Benefits: Enjoy hybrid work options, relocation support, and a competitive salary up to £140,000.
- Why this job: Work with cutting-edge technology and receive mentorship from top experts in the field.
- Qualifications: Experience in robotic planning, control policies, or relevant publications is preferred.
- Other info: Two roles available: Decision-Making Systems Engineer and Imitation Learning/Manipulation Engineer.
The predicted salary is between 84000 - 196000 £ per year.
Location: South of England (Hybrid options available)
Level: Senior
Sponsorship: Available
Salary: Up to £140,000
Work Type: Onsite with real robots
Relocation Support
I'm supporting a well-backed robotics company, founded by repeat entrepreneurs to build one of the most ambitious real-world embodied AI stacks in Europe. They’re applying language + vision + control in a tightly integrated system that’s already moving fast from prototypes to deployment. This isn’t theoretical robotics. These roles are about putting ideas to the test by integrating ML, motion planning, and reasoning into production systems that do useful work in the real world.
TWO OPEN ROLES:
- Decision-Making Systems Engineer
- You'll help shape the brain of the robot, building intelligent bridges between planning, perception, and pretrained models:
- Skill sequencing
- Hybrid planning + learned policies
- LLM prompting as part of control pipelines
- Real-time integration with onboard sensors and motion layers
- Imitation Learning / Manipulation Engineer
- You’ll own high-capacity behaviour models, trained on human demos and sim-to-real environments. From data ingestion to model design to deployment on hardware, you’re the force teaching the robot how to act.
- Large-scale data pipelines
- Vision-language-action learning
- Multitask imitation learning
- Deployment of learned skills on robot arms
Why You’d Want This
- Real hardware, not simulation purgatory
- Direct mentorship from deeply technical founders and an elite team with some of Europe’s sharpest minds in RL, controls, and behaviour learning
- Truly interdisciplinary, if you like hacking agents, models, and mechatronics, this is your playground
Ideal Background
- You don’t need to have done it all, but a few of these should ring true:
- Trained and deployed learned control policies
- Built robotic planning stacks involving LLMs or graph planners
- Debugged complex systems with perception, control, and comms layers
- Published in ICRA, CoRL, RSS, NeurIPS, or similar, and then shipped it
- Interested in safe RL, planning with priors, or diffusion-based policy learning
APPLY NOW to discuss the details and see if this is the right fit for you!
Artificial Intelligence Researcher employer: LinkedIn
Contact Detail:
LinkedIn Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Artificial Intelligence Researcher
✨Tip Number 1
Network with professionals in the robotics and AI fields. Attend conferences, webinars, or local meetups to connect with like-minded individuals and industry leaders. This can help you gain insights into the latest trends and potentially lead to referrals.
✨Tip Number 2
Showcase your hands-on experience with real-world projects. If you've worked on any robotics or AI applications, be prepared to discuss them in detail. Highlight how you integrated machine learning, motion planning, or control systems in practical scenarios.
✨Tip Number 3
Familiarise yourself with the specific technologies and methodologies mentioned in the job description. Brush up on large-scale data pipelines, vision-language-action learning, and imitation learning techniques to demonstrate your expertise during discussions.
✨Tip Number 4
Prepare thoughtful questions about the company's projects and future directions. Showing genuine interest in their work and understanding of their challenges can set you apart from other candidates and demonstrate your enthusiasm for the role.
We think you need these skills to ace Artificial Intelligence Researcher
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in AI, robotics, and machine learning. Focus on projects where you've integrated ML with real-world applications, as this aligns with the company's goals.
Craft a Compelling Cover Letter: In your cover letter, express your passion for robotics and AI. Mention specific projects or experiences that demonstrate your skills in decision-making systems or imitation learning, as these are key areas for the roles.
Showcase Your Publications: If you have published work in relevant conferences like ICRA or NeurIPS, make sure to include these in your application. Highlighting your research can set you apart from other candidates.
Highlight Interdisciplinary Skills: Emphasise any interdisciplinary skills you possess, such as experience in mechatronics or control systems. This role values diverse expertise, so showcasing your versatility can be beneficial.
How to prepare for a job interview at LinkedIn
✨Showcase Your Technical Skills
Be prepared to discuss your experience with machine learning, control policies, and robotic planning stacks. Highlight specific projects where you've integrated these technologies, as practical examples will demonstrate your expertise.
✨Understand the Company's Vision
Research the robotics company and their approach to embodied AI. Familiarise yourself with their products and recent developments in the field. This knowledge will help you align your answers with their goals and show genuine interest.
✨Prepare for Problem-Solving Questions
Expect technical questions that assess your problem-solving abilities. Practice explaining your thought process clearly and concisely, especially when discussing complex systems involving perception and control layers.
✨Demonstrate Interdisciplinary Knowledge
Since the role is interdisciplinary, be ready to discuss how your background in various fields (like machine learning, mechatronics, or behaviour learning) can contribute to the team. Show enthusiasm for collaborating across disciplines.
