At a Glance
- Tasks: Lead cutting-edge research in reinforcement learning for real-world energy optimisation.
- Company: Join a pioneering AI company focused on sustainable energy solutions.
- Benefits: Competitive salary, innovative work environment, and opportunities for impactful research.
- Other info: Be part of a dynamic team driving real change in the hydrocarbon industry.
- Why this job: Make a difference in the energy sector with your expertise in AI and optimisation.
- Qualifications: PhD in relevant field and extensive RL research experience required.
The predicted salary is between 60000 - 80000 £ per year.
Applied Computing was founded in 2024 to build Orbital, a physics-informed foundation model for energy operations. We’re live across oil and gas, refineries, and petrochemicals, working towards our mission: sustainable abundance for a growing planet. The hydrocarbon industry keeps the world running. But its complexity has left operators tied to legacy systems, making critical decisions on less than 10% of available data. We built Orbital to change that. It’s a foundation model built specifically for energy that lets companies use AI at scale, harnessing all of their operational data and optimising in real time for any metric. Decisions get faster, operations get safer, and carbon intensity falls. We’ve raised over $32 million, including one of the largest seed rounds for an AI company in the UK. We’re just getting started.
What You’ll Own
- Orbital’s learning-based optimisation and control stack
- RL + control hybrid systems for industrial processes
- Safe and constrained policy learning frameworks
- Simulation environments and digital twin integrations
- Research → production translation for RL systems
- Benchmarking standards for decision-making systems
Requirements
Must-Have Qualifications
- PhD in Computer Science, Robotics, Control, Applied Mathematics, or related field
- First-author publications in:
- Reinforcement Learning
- Control systems
- Sequential decision-making
- 3+ years of hands-on RL research experience
- Strong foundation in:
- Reinforcement Learning (online + offline)
- Optimisation and control theory (MPC, dynamic programming, etc.)
- Deep learning (PyTorch)
- Experience with:
- Real-world deployment of ML systems
- Simulation environments or digital twins
- Working with noisy, real-world data
How We Work
- Research is judged by production impact, not paper count
- We optimise for real systems, not benchmarks alone
- We value safe, reliable decision-making over theoretical elegance
- Physics, control, and learning are treated as one system
What This Role Is Not
- Not toy RL environments (Atari, MuJoCo-only thinking)
- Not unconstrained policy learning without safety guarantees
- Not offline research disconnected from deployment
- Not a support role; this position owns core optimisation IP
Reinforcement Learning Researcher employer: Applied Computing Technologies
At Applied Computing, we pride ourselves on being an innovative employer that champions a culture of collaboration and real-world impact. Our commitment to employee growth is evident through our focus on cutting-edge research and the application of AI in the energy sector, providing unique opportunities for professional development in a rapidly evolving field. Located at the forefront of sustainable technology, we offer a dynamic work environment where your contributions directly influence the future of energy operations.
Contact Details:
Applied Computing Technologies Recruitment Team
StudySmarter Expert Advice🤫
We think this is how you could land Reinforcement Learning Researcher
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. We all know that sometimes it’s not just what you know, but who you know that can help you land that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to reinforcement learning and optimisation. We love seeing real-world applications of your work, so make sure to highlight any simulations or digital twins you've developed.
✨Tip Number 3
Prepare for interviews by brushing up on your knowledge of RL systems and control frameworks. We want to see how you think on your feet, so practice explaining complex concepts in simple terms. Mock interviews can be a great way to get comfortable!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we’re always on the lookout for passionate individuals who align with our mission of sustainable abundance.
We think you need these skills to ace Reinforcement Learning Researcher
Some tips for your application 🫡
Show Off Your Research:Make sure to highlight your PhD and any first-author publications you've got under your belt. We want to see your hands-on experience in reinforcement learning and how it ties into real-world applications.
Tailor Your Application:Don’t just send a generic CV and cover letter. Tailor them to reflect how your skills and experiences align with our mission at Applied Computing. Show us you understand the hydrocarbon industry's challenges and how you can help solve them.
Be Clear and Concise:When writing your application, keep it clear and to the point. We appreciate straightforward communication that gets right to the heart of your qualifications and passion for the role.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets seen by the right people. Plus, it shows us you’re genuinely interested in joining our team!
How to prepare for a job interview at Applied Computing Technologies
✨Know Your Reinforcement Learning Inside Out
Make sure you’re well-versed in the latest advancements in reinforcement learning, especially those relevant to real-world applications. Brush up on your understanding of online and offline RL, as well as optimisation and control theory, so you can confidently discuss how these concepts apply to the role.
✨Showcase Your Research Impact
Prepare to discuss your previous research projects and how they’ve translated into practical applications. Be ready to explain the impact of your work on real systems, not just theoretical models. This will demonstrate that you align with the company’s focus on production impact over paper count.
✨Familiarise Yourself with Orbital's Mission
Dive deep into the company’s mission of sustainable abundance and understand how Orbital operates within the hydrocarbon industry. Being able to articulate how your skills can contribute to optimising energy operations will show your genuine interest in the role and the company.
✨Prepare for Technical Questions
Expect technical questions that assess your hands-on experience with RL systems and real-world data. Be prepared to discuss your familiarity with simulation environments and digital twins, as well as any challenges you've faced in deploying ML systems. This will help you stand out as a candidate who can hit the ground running.
