As an AI Research Apprentice you'll push the frontiers of generative and multimodal learning that power our autonomous robots. You will prototype diffusion-based vision models, vision-language architectures (VLAs/VLMs) and automated data-annotation pipelines that turn raw site footage into training gold.

Key Responsibilities

Design and train diffusion-based generative models for realistic, high-resolution synthetic data
Build compact Vision-Language Models (VLMs) to caption, query and retrieve job-site scenes for downstream perception tasks
Develop Vision-Language Alignment (VLA) objectives that link textual work-orders with pixel-level segmentation masks
Architect large-scale auto-annotation pipelines that transform unlabeled images / point-clouds into high-quality labels with minimal human input
Benchmark model performance on accuracy, latency and memory for deployment on Jetson-class hardware; compress with distillation or LoRA
Collaborate with perception and robotics teams to integrate research prototypes into live ROS 2 stacks

Qualifications & Skills

Strong foundation in deep learning, probabilistic modeling and computer vision (coursework or research projects)
Hands-on experience with diffusion models (e.g., DDPM, Latent Diffusion) in PyTorch or JAX
Familiarity with multimodal transformers / VLMs (CLIP, BLIP, Flamingo, LLaVA, etc.) and contrastive pre-training objectives
Working knowledge of data-centric AI: active learning, self-training, pseudo-labeling and large-scale annotation pipelines
Solid coding skills in Python, PyTorch / Lightning, plus git-driven workflows; bonus for C++ and CUDA kernels
Bonus: experience with on-device inference (TensorRT, ONNX Runtime) & synthetic data tools (Isaac Sim)

Why Join Us

Research bleeding-edge generative & multimodal tech and watch it land on real construction robots
Publish, patent and open-source: we encourage conference submissions and community engagement
Help build a company from the ground up—your experiments can become flagship product features

Requirements

PyTorch or JAX
C++
CUDA kernels
ONNX Runtime
TensorRT
Isaac Sim
Latent Diffusion

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