PyTorch · ROS
The AI Teams at Agile Robots are looking for a Machine Learning Engineer (m/f/d) VLA Training & Evaluation , who will build the frameworks for fine-tuning and benchmarking robot foundation models on collected robot data. The frameworks are meant to work across different foundation models and for teams with modest data and compute.
Responsibilities
- Training Workflows: Build reusable, model-agnostic training and evaluation workflows, with experiment tracking, model versioning, and checkpoint management, so that different foundation models can be swapped in without rebuilding the pipeline.
- Fine-Tuning: Implement fine-tuning of pretrained vision-language-action models on collected robot data, for example with parameter-efficient methods such as LoRA or adapters, or with distillation.
- Policy Training: Build training loops for action-chunking and transformer-based action models, diffusion policies, and VLM/VLA heads.
- Benchmarks: Build benchmarks with generalization splits, success metrics, and failure-analysis templates.
- Transfer Studies: Work with simulation engineers on studies, including metrics and ablations, that test whether improvements in simulation realism improve performance on the real robot.
- Research Baselines: Turn published methods into working baselines and templates that others can extend.
Essential Skills
- Academic Background: Degree in Computer Science, Machine Learning, Robotics, or a related field (Master’s or PhD typical), or an equivalent combination of formal training and demonstrated experience training deep learning models.
- Training Int