Reinforcement Learning Engineer
London office, Vauxhall
Full time, 5 days in office
Our Mission
Since 2020, Consigli has been on a mission to fundamentally change how real estate and construction work — for the better. These industries have suffered decades of declining efficiency, high risk, and constant friction. We believe it's time to build faster, smarter, and more sustainably.
So we flipped the traditional value chain upside down — and jumped on it.
Engineering, once a late-stage bottleneck, is now a fast, AI-powered front-end enabler.
The autonomous engineer has already helped many real estate devolpers and general contractors reduce material usage in buildings by 20% — a game-changing leap in sustainability and competitiveness for an industry responsible for nearly 40% of global material consumption.
Now, we’re looking for a Reinforcement Learning Engineer to push this revolution even further.
What You’ll Do
Design and implement Reinforcement Learning algorithms to solve real-world engineering and optimization problems
Work with frameworks like Stable Baselines3 to accelerate experimentation and deployment
Build environments and simulations that model complex design decisions in construction and real estate
Develop RL pipelines integrated with production-grade ML systems and our SaaS platform
Collaborate closely with domain experts, data scientists, and software engineers to embed learning agents into high-stakes, real-time applications
Contribute across the full lifecycle: from data prep and reward shaping to training, evaluation, and deployment
Stay up to date with cutting-edge RL research and adapt it pragmatically to engineering contexts
Must-Have Qualifications
Master’s or PhD in Computer Science, Engineering, Applied Mathematics, or similar
Strong experience in Python and popular RL libraries (e.g. Stable Baselines3, Gym, Ray RLlib)
Solid understanding of RL algorithms, exploration strategies, reward engineering, and policy/value function learning
Experience deploying RL models in real or simulated production environments
Ability to communicate technical trade-offs clearly and work collaboratively across disciplines
Problem-solver with a pragmatic mindset: creative, curious, and grounded
Hard-working team player who thrives in high-pace environments
Nice-to-Haves
Familiarity with mathematical optimization, operations research, or control theory
Knowledge of mechanical, civil, or HVAC engineering domains
Experience integrating RL with digital twin environments, CAD tools, or building systems
Our Hiring Process
15-minute screening call
Questionnaire
Coding case: Solve a task that tests your RL and problem-solving skills
On-site interview with our leadership team: CEO, Head of Product, and CTO
Final culture fit check with our CEO
Why Join Consigli?
Shape the future of engineering tech with a team of experts who love what they do
Be part of a company where your work matters, and your ideas become real
Collaborate with sharp, driven colleagues in a culture of trust, ownership, and high standards
Enjoy a dynamic hybrid setup, balancing focus and collaboration
Make a tangible impact on the built environment