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Animore

Senior AI/ML Engineer

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeMunich, GermanyNo salary listed

About the Job

Requirements

  • A Master's or PhD in Computer Science, Robotics, Electrical Engineering, AI, or a related field, with at least 5 years of industry experience applying that research to production systems.
  • A demonstrated track record of shipping machine learning systems that operate at real scale or in the real world.
  • Deep, hands-on expertise with transformer-based architectures, including large language models, vision-language models, and multimodal models.
  • Strong software engineering skills in clean code, testing, system design, and continuous integration and continuous delivery, alongside strong Python and C++ skills.
  • Production experience with PyTorch, TensorFlow, or JAX at scale.
  • A strong mathematical and statistical foundation.
  • Experience with reinforcement learning or imitation learning applied to real systems.
  • Comfort operating with significant autonomy and navigating ambiguity.

Responsibilities

  • Design and train foundation models that integrate vision, language, and actions for embodied intelligence.
  • Adapt large language models and vision-language models 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, 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.
  • Advance the state of the art and bring it into production by staying current with research literature, ideally contributing to it, and translating advances into systems that ship.

Desired Qualifications

  • Direct robotics experience in perception, control, or manipulation systems.
  • Experience with distributed training at scale, including multi-node training and large clusters.
  • Kubernetes or large-scale deployment systems in production.
  • A recognized research profile, such as publications, patents, or open-source contributions with meaningful traction in the AI and robotics community.

About the Company

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