Senior Engineer (m/f/d)
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Key skills for this role
About the Role
Halian is seeking a Senior Engineer to define and advance learning-based approaches for motion planning in autonomous systems. The role involves technical roadmap ownership, model development using imitation learning and reinforcement learning, system integration, and evaluation.
Key Skills for This Role
Responsibilities
- Define and execute a clear roadmap for learning based planning over a 12–24 month horizon
- Design, train, and refine learning based planning models using imitation learning, reinforcement learning, diffusion models, and hybrid techniques
- Integrate learning based planners into existing planning and control architectures
- Define data needs for training and evaluation, ensuring coverage of scenarios and edge cases
- Develop comprehensive evaluation frameworks to assess learned planners against established baselines
- Lead the full experimentation loop from hypothesis to real world testing and performance analysis
- Work closely with existing planning approaches to determine where learning based methods provide value
Requirements
- 6+ years of experience in motion planning for autonomous systems or mobile robotics, including hands on work with learning based planning approaches
- Demonstrated experience taking learning based planning solutions from concept through to deployment in real world environments
- Strong understanding of classical planning approaches, including model predictive control (MPC), sampling based, and optimization based methods
- Proficiency in C++ and Python, with experience in ROS (ROS1 and/or ROS2)
- Experience with modern machine learning frameworks (e.g., PyTorch) and solid understanding of experimental design and validation
Full Job Posting
Role Overview
- This role focuses on defining and advancing learning based approaches for motion planning in autonomous systems.
- It involves setting the technical direction and delivering solutions that evolve from traditional rule based and optimization driven methods toward data driven techniques such as imitation learning, reinforcement learning, and diffusion based planning.
- The position combines hands on technical leadership with ownership of the roadmap, ensuring that learning based planners are innovative, practical, reliable, and ready for real world deployment.
Key Responsibilities
- Technical roadmap ownership: Define and execute a clear roadmap for learning based planning over a 12–24 month horizon.
- Model development and training: Design, train, and refine learning based planning models using imitation learning, reinforcement learning, diffusion models, and hybrid techniques.
- System integration: Integrate learning based planners into existing planning and control architectures.
- Data strategy and requirements: Define data needs for training and evaluation, ensuring sufficient coverage of scenarios and edge cases.
- Evaluation and benchmarking: Develop comprehensive evaluation frameworks to assess learned planners against established baselines.
- Iterative experimentation: Lead the full experimentation loop from hypothesis to real world testing and performance analysis.
- Hybrid planning strategy: Work closely with existing planning approaches to determine where learning based methods provide value.
Required Experience
- 6+ years of experience in motion planning for autonomous systems or mobile robotics, including hands on work with learning based planning approaches.
- Demonstrated experience taking learning based planning solutions from concept through to deployment in real world environments.
- Strong understanding of classical planning approaches, including model predictive control (MPC), sampling based, and optimization based methods.
- Proficiency in C++ and Python, with experience in ROS (ROS1 and/or ROS2).
- Experience with modern machine learning frameworks (e.g., PyTorch) and solid understanding of experimental design and validation.
Preferred Qualifications
- Experience publishing or delivering solutions involving learning based planning methods such as imitation learning, reinforcement learning, diffusion based models, or world model approaches.
- Background in designing hybrid systems that combine learned components with classical planning or safety mechanisms.
- Familiarity with building robust, real world systems that operate under uncertainty and dynamic conditions.
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