The Role
Lead R&D on diffusion-based generative models and vision-language systems for photorealistic simulation, defect synthesis, and domain adaptation. Build large-scale auto-annotation and synthetic-data pipelines, optimize and compress models for Jetson-class edge devices under ROS2, oversee full project lifecycle, publish research, and mentor junior engineers.
Summary Generated by Built In
This role is for one of the Weekday's clients
Min Experience: 8+ years
Location: Bengaluru
JobType: full-time
Requirements
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.
Qualifications & Skills
- 8+ 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).
AI, Diffusion Models, Python
Good-to-have skillsDDPM, CLIP, Pytorch
Skills Required
- 8+ years in deep-learning R&D or M.S./Ph.D. in CS, EE, Robotics with strong publication record
- Demonstrated expertise in diffusion models (DDPM, LDM, ControlNet)
- Expertise in multimodal transformers / VLMs (CLIP, BLIP-2, LLaVA, Flamingo)
- Proven experience building large-scale data-centric AI workflows (active learning, pseudo-labeling, weak supervision)
- Advanced proficiency in Python and PyTorch (or JAX)
- Experience with scalable training and experiment tooling (PyTorch Lightning, DeepSpeed, Ray)
- Familiarity with edge-AI runtimes and deployment (TensorRT, ONNX Runtime) and CUDA/C++ performance tuning
- Strong mathematical foundation (probability, information theory, optimization)
- Experience optimizing/compressing models (INT8 quantization, LoRA, distillation) for Jetson-class edge devices
- Experience with synthetic data generation in Isaac Sim or robotics perception stacks (ROS2, Nav2, MoveIt 2, Open3D)
- Publication record and ability to publish external conference papers; mentoring interns/junior engineers
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The Company
What We Do
Weekday is an AI-powered recruitment platform that helps startups hire top-tier engineering and product talent. By leveraging a massive database of white-collar professionals and advanced outreach tools, the company streamlines the hiring process through automated sourcing, AI-driven resume screening, and white-glove contingency services. Their mission is to modernize recruitment by enabling companies to discover and engage passive candidates efficiently, ensuring high-quality hires for critical roles.
