Research Engineer

PlusAI is a Physical AI company pioneering AI-based virtual driver software for factory-built autonomous trucks. Headquartered in Silicon Valley with operations in the United States and Europe, Plus was named by Fast Company as one of the World’s Most Innovative Companies. Partners including TRATON GROUP’s Scania, MAN, and International brands, Hyundai Motor Company, Iveco Group, Bosch, and DSV are working with Plus to accelerate the deployment of next-generation autonomous trucks. If you’re ready to make a huge impact and drive the future of autonomy, Plus is looking for talented individuals to join its fast-growing teams.
 

You will join our core AI team at the frontier of autonomous decision-making, building the Vision-Language-Action (VLA) models that form SuperDrive's reasoning layer. You'll train VLA models that generate high-level driving decisions and trajectory guidance for on-board strategic decision-making, and design the knowledge distillation and compression techniques that transition large models onto on-board compute.

 

Responsibilities

•  Design, train, and evaluate Vision-Language-Action models that generate high-level driving decisions and trajectory guidance in support of Plus's reasoning layer.
•  Own a VLA workstream end to end — data, architecture, large-scale training, and on-vehicle validation.
• Build training and evaluation pipelines and rigorous metrics for VLA performance in driving contexts.
• Develop distillation and compression recipes to deploy large reasoning models on on-board compute.
• Apply SFT and RL post-training to improve reasoning, robustness, and long-tail behavior.
• Collaborate with perception, planning, and platform teams to bring models from research to production

Required qualifications

• M.S. minimum, Ph.D. preferred in CS, EE, Mathematics, Statistics, or a related field.
• 3+ years implementing and training models in a deep learning framework (PyTorch, TensorFlow, or JAX).
• Direct, hands-on experience training vision-language / vision-language-action models.
• Hands-on experience with model training, evaluation, and deployment in production.
•  Thorough understanding of state-of-the-art vision-language / VLA models, diffusion, flow matching, and transformers.
• Experience with large-scale / distributed model training.

Preferred Qualifications

• Model distillation, quantization, and inference optimization (ONNX/TensorRT, mixed precision, custom kernels).
• SFT and RL post-training of large multimodal models.
• Hands-on experience with multi-modal sensor data (camera, LiDAR, radar).