Staff ML Engineer, VLA Training

Nidus is building autonomous manufacturing systems powered by AI and robotics. U.S. manufacturers are under growing pressure to increase output while navigating workforce constraints, underutilized equipment, and increasingly complex supply chains. We believe the next generation of manufacturing will be enabled by more intelligent, flexible automation—systems that help people and machines work together to expand capacity and improve productivity. We're building the intelligence layer that makes that possible. Our team comes from the intersection of defense technology and AI. We've built and shipped products to DoD customers, and we bring deep experience across ML, robotics, product development, and defense. We're a small, in-person team in New York City working on hard problems that matter. If you want to build the future of American manufacturing, we'd love to hear from you.

About the role

We are building intelligent robots that can perceive the world, understand instructions, and perform useful physical tasks in dynamic, real-world environments. We are looking for a Staff Machine Learning Engineer to develop and scale the vision-language-action models that power our robots. You will own major parts of the model development lifecycle—from dataset construction and training recipes to distributed training infrastructure, evaluation, and on-robot validation. This is a hands-on role at the intersection of machine learning research, large-scale systems, and robotics. You will work closely with researchers, robotics engineers, data teams, and operators to turn new modeling ideas into reliable robot capabilities. The right person is equally comfortable investigating why a policy failed on a robot, designing the next training experiment, and improving the infrastructure required to run that experiment efficiently at scale.

Responsibilities

• Train and improve vision-language-action models for robotic manipulation and other embodied tasks.
• Develop model architectures and training recipes spanning imitation learning, behavior cloning, transformer- and diffusion-based policies, multimodal pretraining, and post-training.
• Build scalable pipelines for pretraining, fine-tuning, evaluation, checkpointing, and model release.
• Train models across multi-node GPU clusters while improving utilization, throughput, stability, and cost efficiency.
• Design data mixtures, sampling strategies, augmentations, and curriculum approaches for large, heterogeneous robot datasets.
• Develop data loaders and preprocessing systems for synchronized video, language, robot state, actions, and other sensor modalities.
• Create reproducible experimentation systems, including configuration management, dataset and model versioning, experiment tracking, and automated regression testing.
• Define offline and on-robot metrics that measure task success, generalization, robustness, latency, safety, and failure modes.
• Build tools for inspecting trajectories, visualizing model behavior, comparing experiments, and diagnosing data or training failures.
• Run structured experiments on physical robots and use the results to guide model, data, and infrastructure improvements.
• Partner with data collection teams to identify coverage gaps, improve demonstration quality, and prioritize collection based on model performance.