Position Overview
This role focuses on the design and development of advanced decision-making frameworks for autonomous systems. It combines traditional logic-based approaches with modern machine learning techniques to create intelligent, adaptive behavior in complex environments. The position requires building scalable solutions that ensure reliable, interpretable, and efficient system behavior across a range of operational scenarios.
Responsibilities
- Develop and refine decision-making architectures that combine rule-based logic with learning-driven models to handle complex and dynamic situations
- Build high-level behavior planning and mission management frameworks to support autonomous system operations
- Design adaptive deliberation mechanisms capable of adjusting to different system dynamics and environmental conditions
- Implement and maintain comprehensive testing strategies, including unit, functional, and integration tests, ensuring system robustness and code quality
- Evaluate and benchmark different decision-making strategies, comparing classical approaches with machine learning-driven methods in terms of safety, performance, and reliability
- Integrate decision modules seamlessly with broader system components, ensuring consistent behavior across the full stack
- Produce clear technical documentation to support development, deployment, and knowledge transfer
- Contribute to implementation using programming languages such as C++ and Python within robotics-oriented frameworks
- Participate in real-world testing to validate system performance and refine decision logic
Required Expertise
- Strong background in decision-making algorithms for autonomous or robotic systems
- Deep understanding of frameworks such as behavior trees, finite state machines, utility-based systems, and planning approaches (e.g., PDDL)
- Familiarity with both classical decision logic and modern data-driven or machine learning-based techniques
- Experience working with autonomous platforms, including multi-agent or multi-vehicle environments
- Proficiency in debugging, profiling, and optimizing performance-critical applications (e.g., using tools like gdb, valgrind, or similar)
- Solid programming skills in C++ and/or Python
- Experience with Linux-based systems, version control (Git), containerization (Docker), and modern development workflows
Additional Qualifications
- Degree in Robotics, Computer Science, Engineering, or a related technical discipline (Bachelor’s, Master’s, or PhD)
- Strong analytical and problem-solving skills, with attention to detail in complex system design
- Ability to communicate technical concepts clearly and collaborate effectively across disciplines
- Self-driven mindset with the ability to manage complex tasks and deliver results in dynamic environments
- Commitment to staying current with advancements in autonomous systems and artificial intelligence
Ideal Candidate Profile
- Systems thinker capable of balancing performance, safety, and interpretability
- Comfortable working across both algorithm design and real-world system integration
- Proactive in identifying improvements and driving innovation in decision-making architectures
- Capable of translating theoretical models into reliable, production-ready solutions
