# AI Research Apprentice

**Company:** [Origin](http://jobs.workable.com/companies/i6CRnMYNgc1yzpDvadsnAg.md)
**Location:** Bengaluru, India
**Workplace:** on site
**Department:** Computer Visions & AI

[Apply for this job](http://jobs.workable.com/view/78724e0b-3d7d-4a44-914c-ef1d91c448d9)

## Description

### About Origin

[Origin](https://origin.tech/) (previously 10xConstruction) is building general-purpose autonomous robots for US construction to tackle rising costs, safety risks, and labor 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 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.

## Requirements

### **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).

###   
Preferred Experiences

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

## Benefits

-   Gain experience in a dynamic startup environment at the forefront of robotics and AI innovation.
-   Contribute to the development of technology that will revolutionize the construction industry.
-   Work alongside a talented and passionate team committed to making a real-world impact.
-   Learn from experienced professionals and gain valuable skills in robotics software engineering.
-   Gain practical experience in a real-world engineering environment.
-   Contribute to a project with the potential to make a significant impact on the construction industry.