AI Research Apprentice

8 hours ago

Bengaluru, Karnataka, India 10x Full time US$ 80,000 - US$ 1,50,000 per year

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 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.

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

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