Job
- Level
- Experienced
- Job Field
- Software, Embedded
- Employment Type
- Part Time/Full Time
- Contract Type
- Permanent employment
- Location
- Working Model
- Onsite
Job Summary
In this role, you will develop AI systems for physical automation tasks, work on transferring simulations to real hardware, and refine machine learning models for complex robotic applications.
Job Technologies
Your role in the team
- You are part of our research team and develop AI systems that operate in the physical world.
- You work on problems at the intersection of Machine Learning, Robotics, and Industrial Automation — where the gap between simulation and reality presents the greatest challenge.
- Development and production-related deployment of AI systems for physical manipulation and automation tasks in close collaboration with mechanics, actuation, and system development (within the framework of a co-design approach).
- Design and training of machine learning and reinforcement learning architectures for contact-rich, high-dimensional systems
- Development and operation of simulation and training pipelines (including GPU-accelerated physics simulation)
- Transfer of AI models from simulation to real hardware (Sim to Real) as well as validation in an industrial environment
- Development and responsibility for scalable infrastructure (e.g., parallel simulations, automated evaluation pipelines, MLOps tooling)
- Presentation of results at industry conferences at the appropriate level of maturity
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Our expectations of you
Education
- Completed degree in Computer Science, Robotics, Mechatronics, Mathematics, Physics, or a comparable field of study.
Qualifications
- In-depth knowledge of reinforcement learning for continuous, high-dimensional action spaces, e.g., policy gradient methods, model-based deep reinforcement learning, or hybrid approaches.
- Very good command of English
- Quick comprehension combined with good communication skills and understanding of customer needs
Experience
- Several years of experience in the development, training, and production deployment of AI/ML models.
- Experience with complex software projects including GPU-accelerated simulation, multi-stage training pipelines, and hardware integration, ideally in the field of control and regulation technology.
- Very good knowledge of Python, experience with C++ is desirable
- Proficient in modern ML frameworks such as PyTorch, with ideally experience in TensorFlow, JAX, CUDA kernels, or model quantization.
- Experience with current AI approaches for physical systems, such as model-free/model-based Deep Reinforcement Learning or Diffusion Models, as well as experience working with 3D data and representations, e.g., point clouds, implicit representations, or neural geometry.
- Ideally, experience with generative design or topology optimization.
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What we offer
- The position can be filled on a full-time or part-time basis.
- For a part-time position, a minimum part-time rate of 60%, which means a workload of at least 21 hours per week, is required.
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Benefits
More net
Work-Life-Integration
Topics that you deal with on the job
This is your employer
Festo AG
Festo is a global player and an independent family-owned company with headquarters in Esslingen am Neckar, Germany. The company supplies pneumatic and electrical automation technology to 300,000 customers of factory and process automation in over 40 industries. The products and services are available in 176 countries.
Description
- Founding year
- 1925
- Company Type
- Established Company
- Working Model
- Full Remote, Hybrid, Onsite
- Industry
- Industry, Production, Engineering Industry
AI Systems Engineer
- Location
- Working Model
- Onsite
- Diversity
- Open for all genders
