Sajjad Pakdamansavoji
I am an AI Research Scientist with 6+ years of experience training, post-training and evaluating foundation models, from LLM agents and VLMs to robot VLAs and world models. I lead teams that take models from data to deployment, and my focus now is multimodal agents that learn continually from real-world feedback.
Currently, as Lead AI Research Scientist at Upwork, I lead a five-person team that owns the job-post generation model behind Upwork's AI agent, serving over 1M daily users. We replaced a paid API model in production with a post-trained Qwen3 LLM that is cheaper and rated higher, and I drive continual learning for the agent through a data flywheel built on gradient matching, with LLM-as-a-judge evaluation and online A/B tests.
Before that, at Huawei Noah’s Ark Lab, I worked on Embodied AI. I post-trained robot VLAs (π0.5, GR00T, OpenVLA) and DiT world models, built diffusion-based synthetic data engines for robot learning, and developed zero-shot 6D/9DoF pose estimation for warehouse robots. Our team, The North Star, won 1st place in the NeurIPS 2026 BEHAVIOR Challenge after placing 4th in 2025.
At the Vector Institute, I turned LLM and VLM research into agentic systems. I architected multi-step reasoning workflows and RAG pipelines that significantly improved retrieval accuracy and safety. To support the broader AI ecosystem, I led workshops on advanced fine-tuning using LoRA and high-throughput deployment via vLLM.
At Trans-Plan, I built 3D perception systems for urban environments. I led the distributed fine-tuning of ViT-based foundation models for open-vocabulary detection, segmentation, and Tracking, boosting tracking accuracy (MOTA) by 28% and achieving 92% accuracy in trajectory classification. I also developed a real-time 3D visualization GUI to enable human-in-the-loop model refinement.
My early work at AVIR involved engineering a multi-modal translation system for Iranian Sign Language. I built a pipeline using MediaPipe and Attention-based Bi-LSTM models for video-to-text translation, optimizing the system to run at >30 FPS for low-latency, real-world use.
I hold an MSc in Computer Science from York University, and a BSc in Electrical Engineering from the University of Tehran. My academic journey has been recognized by prestigious awards, including the VISTA Scholarship, the Vector Scholarship in AI, the Lassonde Entrance Scholarship, and the Founder's Entrance Scholarship. I was also honored with the IEEE ITSC Best Application Paper Award and the Academic Excellence Award from York University.