ML Engineering Intern: Embodied AI for Robotic Control

Laelaps AI; Zürich; Vor Ort; CHF 28.000 - 47.000; Vollzeit

Benefits dieser Stelle

Ownership

Mission driven

Career growth

PhD founders

Competitive equity

International team

Zusammenfassung

Laelaps AI in Zürich, Switzerland is seeking a ML Engineering Intern to research and apply foundation models for robot control, contextual reasoning, and language interaction. You will work at the frontier of embodied AI, taking state-of-the-art VLA and VLM research toward deployment on physical robots.

The role is highly practical: run experiments, build data pipelines, fine-tune large models, and bridge the gap between theory and real-world robot action.

Qualifikationen

• Currently pursuing a PhD or recently completed a Master's in Machine Learning, Robotics, Computer Vision, or a closely related field.
• Strong PyTorch (or JAX) skills, with experience training or fine-tuning large models.
• Familiarity with the current VLA/VLM landscape (π0.5, GR00T, or similar).
• Good coding skills in Python.
• Comfortable using Docker and Git in your workflows.

Aufgaben

• Run experiments with VLA/VLM models for robot control and scene understanding.
• Build data pipelines for fine-tuning foundation models on robotics tasks.
• Evaluate models against real-world tasks and edge cases.
• Contribute to the integration of learned components with classical autonomy modules.
• Optimize models for edge deployment: quantization, distillation, latency tuning.
• Apply solid engineering practices: experiment tracking, reproducibility, evaluation discipline.

Kenntnisse

PyTorch

JAX

Python

Ausbildung

PhD candidate or MS in ML/Robotics/Computer Vision

Tools

Docker

Git

Jobbeschreibung

Laelaps AI in Zürich, Switzerland is seeking a ML Engineering Intern to research and apply foundation models for robot control, contextual reasoning, and language interaction. You will work at the frontier of embodied AI, taking state-of-the-art VLA and VLM research toward deployment on physical robots.

The role is highly practical: run experiments, build data pipelines, fine-tune large models, and bridge the gap between theory and real-world robot action.