Staff ML Engineer, VLA Training at Nidus Technologies

Department: Engineering

Setup: On-site
Location: New York, New York
Type: Full-time
Level: staff+
Posted: Sep 22, 2026

Description

Nidus is hiring a Staff Machine Learning Engineer to develop and scale vision-language-action models for autonomous robots. The role owns model development from datasets and training recipes through distributed training, evaluation, release, and on-robot validation, while building scalable pipelines, experimentation systems, data infrastructure, and production-quality robotics capabilities. The position requires at least five years of professional experience in machine learning, robotics, computer vision, or a related field, along with expertise in deep learning frameworks, transformer and multimodal models, distributed training, and reproducible ML workflows.

Required Skills

PyTorch
JAX
Transformer models
Multimodal models
Distributed training
Experiment tracking
Robotics middleware

Additional Information

Minimum Education: Bachelor’s degree in computer science, machine learning, robotics, electrical engineering, or a related field, or equivalent practical experience.
Required Previous Experiences: Five or more years of professional experience in machine learning, robotics, computer vision, or a closely related field, or equivalent demonstrated impact.
Strong experience training modern deep learning models using PyTorch, JAX, or a comparable framework.
Experience with transformer-based models, multimodal models, generative models, or learned control policies.
Experience building and operating distributed training pipelines on multi-GPU or multi-node accelerator clusters.
Experience with large-scale datasets, high-throughput data loading, experiment tracking, and reproducible ML workflows.
Preferred Qualifications: Experience developing vision-language-action models, vision-language models, or robotics foundation models.
Experience with imitation learning, reinforcement learning, behavior cloning, diffusion policies, action tokenization, or action-conditioned world models.
Experience training policies using data from multiple robot embodiments, task domains, or sensor configurations.
Familiarity with large-scale pretraining, post-training, parameter-efficient fine-tuning, distillation, or model adaptation.
Experience optimizing distributed training using techniques such as FSDP, tensor or pipeline parallelism, mixed precision, gradient checkpointing, or sharded data loading.
Familiarity with robotics middleware and simulation environments such as ROS/ROS 2, MuJoCo, Isaac Sim, or Gazebo.
Experience deploying and evaluating learned policies on physical robotic systems.
Publications or meaningful open-source contributions in machine learning, robotics, computer vision, or related areas.