Computer Vision Engineer (3D / Spatiotemporal AI) – Next-Gen Animal Intelligence Platform
We’re building something that doesn’t exist yet.
At NeuralX, we are pushing the frontier of 3D computer vision and spatiotemporal AI to understand animal behavior and biomass in real-world environments — not in controlled labs, but in complex, dynamic, underwater ecosystems.
This is not a typical “train a model on a clean dataset” role.
You’ll be working on noisy, real-world, multi-camera data, solving problems that sit at the intersection of:
3D reconstruction
Multi-view geometry
Temporal modeling
Behavioral understanding
Applied AI in physical environments
If you like working on problems where the data is imperfect, the physics matters, and the solution isn’t obvious, you’ll enjoy this.
What You’ll Work On
Fine-tuning and improving existing models for:
3D biomass estimation
3D behavioral analysis of animals (trajectory, interaction, patterns)
Multi-camera calibration and synchronization challenges
Spatiotemporal modeling (tracking + sequence understanding)
Handling underwater-specific constraints (visibility, distortion, occlusion)
Improving robustness in production-like environments
Ideal Background
You don’t need to check every box, but strong candidates typically have:
Solid experience in computer vision / deep learning
Hands-on work with:
3D vision (SfM, MVS, NeRF, depth estimation, etc.)
Object tracking / multi-object tracking
Video understanding / temporal models
Strong PyTorch (or equivalent) experience
Ability to debug and iterate in messy, real-world datasets
Bonus points:
Experience with underwater / low-visibility environments
Familiarity with geometry-heavy pipelines
Experience deploying models in production or near-production settings
Why This Is Interesting
You’ll work on real-world impact problems (not benchmark chasing)
The system combines physics + AI + geometry, not just end-to-end black boxes
Opportunity to shape the core intelligence layer of the product
Small, highly technical team — fast iteration, high ownership
Direct exposure to cutting-edge applications of spatial + temporal AI
Engagement
Flexible (project-based → long-term possible)
Remote-friendly, async collaboration
We care more about capability and thinking than credentials
To Apply
Please include:
Relevant projects (GitHub, papers, demos)
Brief explanation of a hard vision problem you solved
Your experience with 3D or temporal modeling (if any)
If you’re excited by solving unsolved problems in the wild, we should talk.
Apply tot his job
Apply To this Job
At NeuralX, we are pushing the frontier of 3D computer vision and spatiotemporal AI to understand animal behavior and biomass in real-world environments — not in controlled labs, but in complex, dynamic, underwater ecosystems.
This is not a typical “train a model on a clean dataset” role.
You’ll be working on noisy, real-world, multi-camera data, solving problems that sit at the intersection of:
3D reconstruction
Multi-view geometry
Temporal modeling
Behavioral understanding
Applied AI in physical environments
If you like working on problems where the data is imperfect, the physics matters, and the solution isn’t obvious, you’ll enjoy this.
What You’ll Work On
Fine-tuning and improving existing models for:
3D biomass estimation
3D behavioral analysis of animals (trajectory, interaction, patterns)
Multi-camera calibration and synchronization challenges
Spatiotemporal modeling (tracking + sequence understanding)
Handling underwater-specific constraints (visibility, distortion, occlusion)
Improving robustness in production-like environments
Ideal Background
You don’t need to check every box, but strong candidates typically have:
Solid experience in computer vision / deep learning
Hands-on work with:
3D vision (SfM, MVS, NeRF, depth estimation, etc.)
Object tracking / multi-object tracking
Video understanding / temporal models
Strong PyTorch (or equivalent) experience
Ability to debug and iterate in messy, real-world datasets
Bonus points:
Experience with underwater / low-visibility environments
Familiarity with geometry-heavy pipelines
Experience deploying models in production or near-production settings
Why This Is Interesting
You’ll work on real-world impact problems (not benchmark chasing)
The system combines physics + AI + geometry, not just end-to-end black boxes
Opportunity to shape the core intelligence layer of the product
Small, highly technical team — fast iteration, high ownership
Direct exposure to cutting-edge applications of spatial + temporal AI
Engagement
Flexible (project-based → long-term possible)
Remote-friendly, async collaboration
We care more about capability and thinking than credentials
To Apply
Please include:
Relevant projects (GitHub, papers, demos)
Brief explanation of a hard vision problem you solved
Your experience with 3D or temporal modeling (if any)
If you’re excited by solving unsolved problems in the wild, we should talk.
Apply tot his job
Apply To this Job