AI Research Apprentice

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 Action Models (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.
PyTorch or JAX C++ CUDA kernels ONNX Runtime TensorRT Isaac Sim Latent Diffusion