* 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