AI Research Engineer – Reinforcement Learning in Portugal.
This role sits at the forefront of applied AI research, focusing on advancing reinforcement learning systems that power next-generation intelligent models.
You will design and optimize algorithms that improve decision-making, adaptability, and performance across complex, real-world environments.
Working in a highly research-driven and experimentation-heavy setting, you will contribute to both foundational RL innovations and production-grade implementations.
The position spans work on efficient models for constrained hardware as well as large-scale multimodal systems integrating text, image, and audio.
You will play a key role in building simulation environments, refining training pipelines, and enhancing policy performance.
This is an opportunity to directly shape cutting-edge AI systems deployed at global scale.AccountabilitiesDesign and implement advanced reinforcement learning algorithms to improve decision-making, policy optimization, and system performance across simulated and real-world environmentsRun controlled experiments, track performance metrics, evaluate outcomes against benchmarks, and iterate on model improvements through empirical analysisDevelop and curate high-quality simulation environments and training datasets aligned with domain-specific requirements and learning objectivesDebug and optimise RL pipelines, addressing challenges such as exploration strategy, reward stability, sample efficiency, and training convergenceCollaborate with engineering and research teams to integrate RL agents into production systems and ensure measurable real-world performance gainsDefine evaluation frameworks and continuously monitor deployed systems to support robustness, scalability, and domain adaptationRequirementsAdvanced degree in Computer Science, Machine Learning, or related field;
PhD preferred with strong academic research background and publications in top-tier conferencesProven experience running large-scale reinforcement learning projects, including modern online RL techniques such as policy optimisation methods and actor-critic frameworksDeep understanding of reinforcement learning theory and practice, including policy gradients, exploration-exploitation trade-offs, and optimisation strategies for stability and efficiencyStrong hands-on expertise with PyTorch and RL frameworks, including building full pipelines from simulation to training and deploymentDemonstrated ability to solve complex RL challenges such as sample inefficiency, reward noise, and training instability through empirical and algorithmic innovationStrong analytical mindset with ability to design robust experiments, interpret results, and continuously improve model performanceBenefitsFully remote work environment with global team collaborationOpportunity to work on cutting-edge AI and reinforcement learning research at scaleHigh-impact role influencing production-level AI systems and real-world applicationsCompetitive compensation aligned with experience and expertiseExposure to advanced research, multimodal AI systems, and state-of-the-art infrastructureFlexible working culture supporting autonomy and innovation#J-*****-Ljbffr