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For Companies At a Glance
- Tasks: Push the boundaries of reinforcement learning and build intelligent agents that learn and adapt.
- Company: Join a cutting-edge tech startup redefining software testing with AI.
- Benefits: Competitive salary, flexible work environment, and the chance to shape the future of technology.
- Why this job: Be at the forefront of AI innovation and make a real impact in the industry.
- Qualifications: 5+ years in ML production, deep RL expertise, and Python/PyTorch mastery.
- Other info: Work alongside elite professionals and enjoy unparalleled career growth opportunities.
The predicted salary is between 36000 - 60000 ÂŁ per year.
Why This Role is Different
Most “AI engineer” jobs are just applying models someone else built. This isn’t that. This is about pushing RL to its edge: Agents that think: networks that see and understand apps through vision, structure, and behavior. Agents that explore: curiosity‑driven RL that uncovers edge cases no human would think of. Agents that learn: smarter with every bug, sharper with every correction. Agents that scale: millions of states, thousands of sessions, decisions in sub‑seconds.
What You’ll Achieve
- In your first three months, you’ll see your reinforcement learning prototypes running live inside real applications, surfacing bugs no human ever noticed.
- By six months, those agents will have evolved, scaling across multiple environments, learning and adapting in ways that prove this isn’t theory but reality.
- And within a year, the intelligence you’ve built will sit at the heart of every release for our first customers, powering their ability to ship AI‑generated code with confidence.
What You Bring (Non‑Negotiables)
- 5+ years shipping ML to production (real systems, not papers).
- Deep RL expertise, you think in Q‑values and policy gradients.
- Experience building autonomous agents that actually work at scale.
- Python/PyTorch mastery.
The Stuff That Matters
- You’re obsessed with solving “impossible” problems.
- You’d rather ship and learn than debate in theory.
- You can explain RL to a CEO and optimize it for a GPU cluster.
- You thrive in chaos and see it as opportunity.
Why Join Now
- Impact: You won’t be “joining a team.” You’ll be the team that defines how software is built in the age of AI. Your code won’t sit in a corner, it will become the backbone of a new category.
- Market: Software testing hasn’t changed in 30 years. AI‑generated code has rewritten the rules overnight. Whoever solves this bottleneck doesn’t just win a market, they reshape the entire industry.
- Team: Small, elite, no passengers. You’ll be working side by side with a CTO who built this at Meta and a founding team that’s scaled some of the fastest‑growing tech companies on the planet.
- Timing: Rarely do technology shifts and career timing line up. This is one of those moments. Five years from now, autonomous QA will be a given. Right now, it’s unsolved, and you could be the one who solves it.
The Challenge
Big tech tried to brute‑force this problem and hit a wall. Most startups never got past brittle scripts. The reason is simple: building true autonomy takes more than patching frameworks, it takes intelligence. That’s the path we’re on. Your system will need to:
- Navigate the chaos of modern web apps.
- Learn from sparse, delayed rewards.
- Balance exploration with validation.
Applied Research Engineer in London employer: Duku AI
Contact Detail:
Duku AI Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Applied Research Engineer in London
✨Tip Number 1
Network like a pro! Get out there and connect with folks in the AI and ML space. Attend meetups, webinars, or even just grab a coffee with someone who’s already in the field. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio that highlights your reinforcement learning projects. Share your prototypes, explain your thought process, and demonstrate how you’ve tackled complex problems. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Don’t just apply; engage! When you find a role that excites you, reach out to the hiring manager or team members on LinkedIn. Ask insightful questions about the role or the company. This shows genuine interest and can help you stand out from the crowd.
✨Tip Number 4
Keep it real! During interviews, be honest about your experiences and challenges. Talk about the bugs you’ve encountered and how you’ve learned from them. Employers love candidates who are not only skilled but also resilient and eager to grow.
We think you need these skills to ace Applied Research Engineer in London
Some tips for your application 🫡
Show Your Passion for AI: When you're writing your application, let your enthusiasm for AI and reinforcement learning shine through. We want to see that you’re not just ticking boxes but genuinely excited about pushing the boundaries of what's possible in this field.
Be Specific About Your Experience: Don’t just say you have 5+ years of experience; tell us about the projects you've worked on and the impact they had. We love details! Highlight your deep RL expertise and any autonomous agents you've built that actually work at scale.
Keep It Clear and Concise: While we appreciate a good story, make sure your application is easy to read. Use clear language and avoid jargon where possible. We want to understand your journey without getting lost in technical terms.
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 don’t miss out on any important updates. Plus, it shows you’re keen to join our team!
How to prepare for a job interview at Duku AI
✨Know Your Reinforcement Learning Inside Out
Make sure you can discuss reinforcement learning concepts fluently. Be prepared to explain Q-values and policy gradients in a way that even a non-technical person could understand. This shows not only your expertise but also your ability to communicate complex ideas clearly.
✨Showcase Your Real-World Experience
Bring examples of your past projects where you've shipped machine learning models to production. Highlight specific challenges you faced and how you overcame them, especially in building autonomous agents at scale. This will demonstrate your hands-on experience and problem-solving skills.
✨Embrace the Chaos
This role thrives in a fast-paced environment, so be ready to discuss how you handle uncertainty and adapt to changing situations. Share stories that illustrate your ability to see opportunities in chaos and how you've successfully navigated complex problems in previous roles.
✨Prepare for Technical Challenges
Expect to tackle some technical questions or even live coding during the interview. Brush up on Python and PyTorch, and be ready to solve problems on the spot. Practising common algorithms and reinforcement learning scenarios can give you an edge and show your readiness for the role.
