AI Research Apprentice

• 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.

• 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).

• 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

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