Research Engineer (Physical AI)
Pubblicato il 24-09-2026 - Generali Group in Italia
Overview
In this role you advance the intelligence layer for autonomous industrial systems, collaborating with AI and engineering leaders. You’ll prototype multi-agent and collective approaches and integrate them with robotic platforms and industrial infra. You design robust experiments to evaluate system-level behavior and work across research and hardware teams to deliver real-world impact. The role blends research excellence with production-ready implementations in a hybrid Trieste environment.
Retribuzione / Benefits
- Lavoro da remoto and flexible hours
- Corporate welfare system
- Meal vouchers
- Supplementary health insurance
- Wellbeing initiatives
- Training and development
Responsabilità
- Research and prototype multi-agent and collective intelligence approaches from simulation to real-system integration
- Build the intelligence and coordination layer interfacing with physical systems and robotics infrastructure
- Design rigorous system-level experiments to validate emergent behavior
- Integrate AI systems with external engineering stacks via clean interfaces and contracts
- Define evaluation frameworks for collective/adaptive behavior and performance degradation
- Stay current with multi-agent, swarm intelligence, and autonomous systems research and translate to applicable solutions
Requisiti fondamentali
- Master's degree or PhD in a STEM field
- At least 3–5 years of experience in multi-agent systems, autonomous systems, or collective intelligence with real implementations
- At least 3–5 years of experience in simulation tools/methodologies ahead of hardware integration
- Experience integrating AI with physical infrastructure or robotic platforms; comfortable with robotic middleware at an operational level
- Entrepreneurial mindset with autonomous, proactive attitude
- Strong collaboration with external engineering teams
- Excellent communication and stakeholder management (tech/UI and executive level)
- LLM-based systems; classical ML; APIs; microservices; inference optimization
- Strong software engineering fundamentals; ability to transition experimental code to production
- Experience at the research/engineering boundary: controlled experiments and production services