Job
- Level
- Experienced
- Job Field
- Data, Embedded
- Employment Type
- Full Time
- Contract Type
- Permanent employment
- Salary
- 55.000 to 75.000€ gross/year
- Location
- Heidelberg
- Working Model
- Hybrid, Onsite
Job Summary
In this role, you design end-to-end AI pipelines for sensor data in robotics and battery applications, combining physics-based models with data-driven approaches and integrating AI models into real-time hardware.
Job Technologies
Your role in the team
- Design and implement end-to-end AI pipelines for time-series and event-driven sensor data across battery and robotics applications.
- Combine physics-based models with data-driven approaches, hybrid and physics-informed machine learning to build systems that are robust, interpretable, and certifiable for industrial deployment.
- Deploy and optimize AI models on embedded and edge hardware, from microcontrollers to edge gateways, with hard latency and memory constraints.
- Collaborate closely with hardware, firmware, and systems engineers to integrate sensor electronics, data acquisition, and AI inference into real devices.
- Develop, validate, and benchmark models for state estimation, anomaly detection, fault classification, and remaining useful life prediction in both battery management and robotic manipulation contexts.
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Our expectations of you
Education
- Master's degree or PhD in Computer Science, Robotics, Electrical Engineering, Applied Mathematics, or a related field.
Qualifications
- Solid understanding of time-series data engineering: synchronization, cleaning, labeling, and handling of large continuous sensor streams.
- Vertrautheit mit Cloud-Infrastruktur und CI/CD für ML-Systeme (MLOps).
- Published research in machine learning, robotics, or sensor intelligence at a leading venue is an advantage.
- Proficiency in English and German.
Experience
- 3+ years of applied machine learning experience, ideally for sensor-based systems in robotics, industrial automation, or energy storage.
- Strong Python and C++ skills; practical experience with PyTorch; JAX experience is a plus for physics-informed modelling.
- Proven experience deploying models to embedded or edge targets; familiarity with edge deployment pipelines (ONNX, TensorFlow Lite, or equivalent).
- Experience with at least two of: reinforcement learning, imitation learning, multimodal foundation models, or physics-informed neural networks.
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What we offer
- At FLEXOO, you can expect more than just a job - you will become part of an innovative environment where your ideas matter and your contributions are visible.
- You are part of a team of experts with the opportunity to shape next-generation AI functionality in sensor-rich systems such as battery storage solutions and robots.
- You will closely collaborate with experts in printed electronics, embedded systems, and industrial monitoring.
- We offer you a long-term position with room to grow into technical leadership for AI in sensor-based products.
- Attractive and performance-based compensation in a future-oriented company.
- Flexible working hours and the option to work remotely one day per week.
- Various benefits (business lunch, free hot and cold drinks, job ticket, etc.).
- Regular team events.
- Modern location in Heidelberg Bahnstadt, 10 minutes from the main train station, free parking.
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Benefits
Work-Life-Integration
Health, Fitness & Fun
Topics You Will Work On
Job Locations
About Your Employer
Flexoo GmbH
Flexoo GmbH, based in Heidelberg, is a significant provider in the field of printed electronics and sensor technology. As an independent spin-off of InnovationLab GmbH, it offers an innovative manufacturing concept characterized by high flexibility, quality, and scalability, enabling mass production of intelligent sensors.
Description
- Company Type
- Startup
- Working Model
- Hybrid, Onsite
- Industry
- Electronics, Automatization
AI Engineer
- Salary
- 55.000 to 75.000€ gross/yearEstimated net salary based on the gross salary in the job ad using tax class I.34.978 to 45.351 € net/year · Tax class I
- Location
- Heidelberg
- Working Model
- Hybrid, Onsite
- Diversity
- Open for all genders
