At a Glance
- Tasks: Build autonomous agents that revolutionise software testing and enhance engineering velocity.
- Company: Join a venture-backed AI company led by industry veterans from Meta, Uber, and Deliveroo.
- Benefits: Equity stake, unlimited resources, and a fast-paced, innovative work environment.
- Why this job: Be at the forefront of AI-driven software quality and make a real impact.
- Qualifications: 5+ years in ML production, deep RL expertise, and Python/PyTorch mastery.
- Other info: Work alongside elite team members and shape the future of software testing.
The predicted salary is between 36000 - 60000 ÂŁ per year.
Change Software Forever. QA slows the world down. Flaky tests kill trust, stall releases, and bleed engineering velocity. Duku AI is ending that era. We’re building autonomous agents that think like engineers: they run every critical user journey, catch failures before users do, and self-heal as the codebase evolves. Real AI teammates, not test scripts that break on impact. We’re venture-backed and led by operators who’ve scaled Meta’s testing infrastructure, launched Uber’s global playbooks, and grew Deliveroo from zero to hypergrowth. We know what elite execution looks like and we’re hunting for one more builder to help us rewrite the rules of software quality.
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.
If you’ve ever wanted to take RL out of papers and into the wild, this is it.
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 align. 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.
- Transfer knowledge across completely different applications.
It won’t be easy. That’s the point.
What You Get
- Equity that actually moves the needle, not token options, but a real ownership stake in what could be the category-defining AI company of the decade.
- Unlimited firepower, the hardware, compute, and resources you need to push RL further than anyone has before.
- A seat at the table, not a cog in the machine, you’ll be in the room where every decision is made, shaping both the product and the company.
- Speed over politics, a London base where execution beats process, every time.
- A shot at legacy, work that will outlive your CV, the kind of achievement you’ll still be talking about 20 years from now.
To win the space, we’re looking for the best people in London, with 10/10 ambition and work ethic to join us and build a product people love.
Applied Research Engineer employer: Duku AI
Contact Detail:
Duku AI Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Applied Research Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those who work at companies you admire. A friendly chat can lead to insider info about job openings or even a referral.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects and expertise in reinforcement learning. This is your chance to demonstrate what you can do beyond just a CV.
✨Tip Number 3
Prepare for interviews by practising common questions and coding challenges related to ML and RL. We all know that confidence is key, so get comfortable with explaining your thought process and solutions.
✨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, it shows you’re genuinely interested in being part of our team.
We think you need these skills to ace Applied Research Engineer
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 software testing.
Be Specific About Your Experience: Don’t just list your skills; give us concrete examples of how you've applied them in real-world scenarios. Talk about the autonomous agents you've built, the challenges you faced, and how you overcame them. We love a good story!
Tailor Your Application: Make sure your application speaks directly to the role of Applied Research Engineer. Highlight your deep RL expertise and experience with Python/PyTorch. Show us how your background aligns with our mission to change software forever.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to see your application in the context of our hiring process. Plus, it shows you’re serious about joining 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, especially Q-values and policy gradients. Prepare to explain how you've applied these in real-world scenarios, as this role demands deep expertise.
✨Showcase Your Problem-Solving Skills
Be ready to share specific examples of 'impossible' problems you've tackled in the past. Highlight your ability to ship solutions quickly and learn from them, as this aligns with the company's fast-paced environment.
✨Demonstrate Your Technical Mastery
Brush up on your Python and PyTorch skills. You might be asked to solve coding challenges or discuss your previous projects, so have concrete examples ready that showcase your technical prowess and experience building autonomous agents.
✨Embrace the Chaos
This role thrives in a chaotic environment, so convey your excitement about tackling complex challenges. Share instances where you've turned chaos into opportunity, showing that you can adapt and innovate under pressure.