who lives and breathes reconstruction pipelines, thrives on geometry challenges, and wants to architect systems that redefine how machines see the world.
This is not a plug-and-play role. You'll be building the
core reconstruction engine
that powers our vision products - designing a
modular, blazing-fast pipeline
where algorithms can be swapped in and out like precision-tuned gears. Think
COLMAP on steroids
, fused with neural rendering, and optimized for scale.
Core Tech Stack:
Languages:
C++, Python
CV/3D Libraries:
OpenCV, Open3D, PCL, COLMAP
Math/Utils:
NumPy, Eigen
Visualization:
Plotly, Matplotlib
Deep Learning:
PyTorch, TensorFlow
Data:
Point clouds, meshes, multi-view image sets.
Desired Expertise:
Core Expertise:
Deep, hands-on knowledge of 3D computer vision fundamentals, including projective geometry, triangulation, transformations, and camera models.
Algorithm Mastery:
Proven experience with point cloud and mesh processing algorithms, such as ICP for registration and refinement.
Development Experience:
Strong software engineering skills, primarily in a Linux environment. Experience deploying applications on Windows (or via WSL) is a major plus.
Data Handling:
Experience managing and analyzing the large datasets typical in 3D reconstruction.
Projective Geometry Mastery:
Camera models, projections, triangulation, multi-sensor fusion.
Transformations:
Rotations, quaternions, coordinate system conversions, 3D frame manipulations.
SfM & MVS:
Proven hands-on with pipelines and dense reconstructions.
Proficiency with 3D visualization tools and libraries (e.g., OpenGL, Blender scripting) for rendering and debugging point clouds and meshes.
Bonus Points:
- You've
built a full 3D reconstruction pipeline from scratch
.- Hands-on with
Gaussian Splatting
or
NeRFs
.- Experience with
SuperGlue
or other state-of-the-art feature matching models.
- Hybrid reconstruction experience: fusing
classical geometry
with
neural methods
.
- Experience with real-time or streaming reconstruction systems.
- Familiarity with emerging topics like 3D scene segmentation and the application of LLMs to geometric data.
- Knowledge of Ukrainian is a great advantage.
What You'll Do:
End-to-End Pipeline Development:
You will architect, build, and deploy a robust, high-performance 3D reconstruction pipeline from multi-view imagery. This includes owning and optimizing all core modules: feature detection, matching, camera pose estimation, SfM, dense stereo (MVS), and mesh/surface generation.
System Architecture:
Design a highly modular and scalable system that allows for interchangeable components, facilitating rapid A/B testing between classical geometric algorithms and modern neural approaches.
Performance Optimization:
Profile and optimize the entire pipeline for low-latency, real-time performance. This involves advanced GPU programming (CUDA/OpenCL), efficient memory management to handle large models, and leveraging modern compute frameworks.
Research & Integration:
Stay at the forefront of academic and industry research. You will be responsible for identifying, implementing, and integrating state-of-the-art methods in SLAM, neural rendering (NeRFs, 3DGS), and hybrid geometry-neural network models.
Data Management:
Develop solutions for handling, processing, and distributing large-scale image and 3D datasets (e.g., using tools like Rclone).
Why Join DeepX?
This is your chance to own a
core engine at the frontier of 3D vision
. You'll be surrounded by a small but elite team, working on
real-world deployments
where your algorithms won't just run in benchmarks - they'll run in
airports, mines, logistics hubs, and beyond
. If you want your code to shape how machines perceive the world at scale,
this is the place
.
Sounds like you? -> Let's talk
Send us your
portfolio, GitHub, or projects
- we love seeing real reconstructions more than polished CVs.
Job Type: Full-time
Pay: 2,200.00-3,700.00 per month
Benefits:
Flexitime
Work from home
Work Location: Remote
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