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# Research Scientist, Video Understanding & World Models

- **Company:** MECKA ASSOC
- **Location:** New York, NY, United States
- **Salary:** $200,000.0 - $250,000.0
- **Contract:** Permanent contract
- **Skills:** Distributed Computing Environment, Language Modeling, Software Engineering, Pytorch, Large Language Models
- **Published:** June 3, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=20e4b31e6189472d

## About the Role

Do you have experience in Software engineering?, * Deep experience training large models in PyTorch (or equivalent), including multi-GPU or distributed training.

 * Strong understanding of modern video representation learning and/or multimodal modeling.
 * Ability to run rigorous experiments and communicate results clearly.

Strong Signals:

 * Experience with video VLMs / VLA-adjacent systems (VideoCLIP, InstructBLIP-Video, LLaVA-Video-class).
 * Experience with egocentric / embodied datasets (Ego4D, EgoExo4D, EPIC-Kitchens, Something-Something).
 * Strong software engineering discipline: you write research code that can be shipped.

## Description

We are looking for a Research Scientist, Video Understanding to own Mecka's video understanding agenda end-to-end: train large-scale video representation and video-language models on our egocentric + stereo corpus, and turn the resulting checkpoints into production signals the rest of the stack ships on.

This role is focused on large model training, video encoders, video-language models, VLMs/VLAs, and temporal representation learning on real-world robotics data., * Own model architecture and training strategy across Mecka's task families (manipulation, locomotion, daily activity, long-horizon behavior).

 * Run self-supervised and multimodal pretraining (VideoMAE / VJEPA / VideoPrism / InternVideo-class) with rigorous evals and clean ablations., * Train and fine-tune video encoders and video-language models (temporal transformers, joint-embedding models, contrastive objectives, masked modeling, instruction/video alignment).
 * Incorporate useful priors (pose, depth, camera motion, optical flow) when it improves representation quality.


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