Lokalizacja
Munich, Niemcy
Tryb pracy
Na miejscu
Forma zatrudnienia
Pełny etat
Poziom doświadczenia
Senior
Kategoria
IT
Kategoria IT
Data Science i ML
Język
English
Opublikowano
28 maja 2026
Ostatnio sprawdzono
28 maja 2026
Kontekst JobGrid
Podsumowanie roli od JobGrid
Senior AI/ML Engineer (F/M/D) at AlphaIgnis: Munich, Niemcy; Na miejscu; Pełny etat; Senior; IT. JobGrid adds normalized role facts, source context, and a path to the employer application page so candidates can compare the listing before applying.
- Location and workplace: Munich, Niemcy, Na miejscu
- Role classification: IT, Data Science i ML, Pełny etat, Senior
- Source freshness: checked by JobGrid on 2026-05-28.
- Application path: candidates continue to the employer application page with non-personal referral tags.
The Opportunity
As a Senior AI/ML Engineer, you will work at the intersection of foundation models and robotics, developing systems that combine vision, language, and action for embodied intelligence.
You will help bridge cutting-edge AI research with real-world robotic applications, ensuring that large-scale models can operate reliably in physical environments. This role sits at the core of building robots that can perceive, reason, and act intelligently in dynamic settings.
Your Responsibilities
- Design and train foundation models that integrate vision, language, and actions for embodied intelligence
- Adapt LLMs and VLMs for robotic control, planning, and interactive behavior, enabling context-aware decision making
- Develop AI-driven control policies for manipulation, grasping, and motion planning using reinforcement learning, imitation learning, and foundation model approaches
- Build modular, scalable, and high-performance data processing, training, and inference pipelines for large-scale datasets
- Design reproducible workflows for training, evaluation, and deployment, including benchmarking for generalization, safety, and task success
- Stay current with advances in AI and robotics, translating research into production systems and contributing to papers, patents, or open-source work
Technologies
- Python, C++
- PyTorch (or TensorFlow / JAX)
- Transformer-based models (LLMs, VLMs, multimodal architectures)
- Distributed training frameworks
- Linux, Docker (Kubernetes nice to have)
- ML pipelines and model deployment systems