AlphaIgnis

Senior AI/ML Engineer (F/M/D)

🇩🇪 Munich, Niemcy Na miejscu IT Pełny etat Senior Opublikowano Maj 28, 2026
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