Senior Applied Research Scientist – Reinforcement Learning

Vedi l’offerta su Go Lavoro: golavoro.it/annuncio-lavoro/7kn1xq

About Equixly
Equixly is an Italian cybersecurity company founded to revolutionise API security through proprietary artificial intelligence and machine learning algorithms. Our platform automates the API Security Testing process, enabling companies to identify deep vulnerabilities rapidly, scalably, and continuously.
On a strong growth trajectory, we are building out our AI Team with talented engineers and scientists who want to work on real, production-grade LLM systems and high-complexity technical challenges at the cutting edge of applied AI.
The role
We are looking for a Senior Applied Research Scientist specialising in Reinforcement Learning. You will develop learning strategies for autonomous cybersecurity agents that explore complex environments, select actions, use tools, and improve through feedback.
This is an applied research role: the goal is to turn advanced research into working prototypes, validated experiments, and production-grade capabilities. You will report to the Head of AI.
What you will do
- Lead applied RL research for exploration, planning, tool use,



and long-horizon decision-making.
- Define environments, states, actions, reward functions, and evaluation criteria.
- Develop training strategies for sparse or delayed rewards, credit assignment, safety, and robustness.
- Build reproducible prototypes and work with AI engineers and cybersecurity experts to integrate successful methods into the product.

What we are looking for
- PhD in Computer Science, Artificial Intelligence, Machine Learning, Robotics, Autonomous Systems, Control, or a related discipline, with research focused primarily on Reinforcement Learning.
- A strong RL research track record, demonstrated through peer-reviewed publications and hands-on experimental work.
- Knowledge of modern RL methods, including value-based and policy-gradient methods, actor–critic architectures, model-based or model-free RL, offline RL, hierarchical RL, or multi-agent RL.
- Practical experience wit

Vedi l’offerta su Go Lavoro: golavoro.it/annuncio-lavoro/7kn1xq