Machine Learning Engineer (m/f/d) VLA Training & Evaluation.

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 Internals: Hands-on experience writing and debugging training loops in PyTorch or JAX, including data loading, loss design, and optimizer behavior.
• Fine-Tuning: Experience fine-tuning pretrained models and adapting them to new tasks and data.
• Robot Learning: Working understanding of how imitation-learned policies, such as action-chunking or diffusion policies, turn observations into robot actions.
• Experiment Management: Experience designing evaluations and ablations, and running experiments with tracking, versioning, and checkpoint management so that others can reproduce them.

Beneficial Skills

• VLA Models: Experience fine-tuning vision-language-action or vision-language models for robot control.
• Real-Robot Evaluation: Experience evaluating learned policies on physical robots.
• Sim-to-Real: Familiarity with measuring how well models trained in simulation transfer to real hardware.