About Origin
Origin (previously 10xConstruction) is building general-purpose autonomous robots for US construction to tackle rising costs, safety risks, and labour shortages. Our modular, multi-trade platform combines purpose-built hardware with real-time site intelligence to navigate complex environments and execute tasks with precision. Trained in high-fidelity simulation and already deployed on live sites, our robots deliver 5x faster execution, 250%+ margin expansion, and significant cost savings. Join India’s most talent-dense robotics team consisting of individuals from IITs, Stanford, UCLA, etc.
About the Role
As a core member of the AI Research team you'll turn cutting-edge, vision-language and diffusion advances into robust real-time systems that see reason and act on dynamic construction sites.
Key Responsibilities
- Research & innovate diffusion-based generative models for photorealistic wall-surface simulation, defect synthesis and domain adaptation.
- Architect and train Vision-Language Models (VLMs) and Vision-Language Action Models (VLA) objectives that connect textual work orders, CAD plans and sensor data to pixel-level understanding.
- Lead development of auto-annotation pipelines (active learning, self-training, synthetic data) that scale to millions of frames and point-clouds with minimal human effort.
- Optimize and compress models (INT8, LoRA, distillation) for deployment on Jetson-class edge devices under ROS 2.
- Own the full lifecycle—problem definition, literature review, prototyping, offline/online evaluation and production hand-off to perception & controls teams.
- Publish internal tech reports and external conference papers; mentor interns and junior engineers.
Requirements
Qualifications & Skills
- 3+ years in deep-learning R&D or Ph.D./M.S. in CS, EE, Robotics or related field with strong publication record.
- Demonstrated expertise in diffusion models (DDPM, LDM, ControlNet) and multimodal transformers / VLMs (CLIP, BLIP-2, LLaVA, Flamingo).
- Proven success building large-scale data-centric AI workflows—active learning, pseudo-labeling, weak supervision.
- Advanced proficiency in Python, PyTorch (or JAX), experiment tracking and scalable training (PyTorch Lightning, DeepSpeed, Ray).
- Familiarity with edge-AI runtimes (TensorRT, ONNX Runtime), and CUDA / C++ performance tuning.
- Strong mathematical foundation (probability, information theory, optimization) and ability to translate theory into production code.
- Bonus: experience with synthetic data generation in Isaac Sim or robotics perception stacks (ROS2, Nav2, MoveIt 2, Open3D)
