Nextologies is an international company specializing in telecommunications and video content delivery and processing solutions. As we expand our team, we are looking for a Computer Vision & 3D Engineer for full time or contract based position.
About the role
We're building a real-time visual replacement pipeline that operates at broadcast frame rates (60fps+). The system ingests live video, performs geometric scene understanding, reconstructs portions of the scene using modern neural rendering techniques, and composites replacement content seamlessly — all in real time.
This is a deep technical role at the intersection of computer vision, neural scene representation, and GPU-accelerated rendering. You'll be working on production-grade infrastructure where latency and visual quality are both non-negotiable.
What you'll work on
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Camera pose estimation and geometric localization from video frames
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Neural scene reconstruction using Gaussian splatting (3DGS / 4DGS) — including capture, training, and real-time inference pipelines
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Real-time inpainting and compositing on reconstructed scene geometry
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GPU pipeline optimisation for sub-frame latency on NVIDIA hardware
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Training data generation for supporting ML models (synthetic image generation, perspective transforms, augmentation pipelines)
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Integration with broadcast video infrastructure (SDI, NDI, or similar)
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Building and maintaining production services on Linux — deployment, monitoring, profiling, and debugging in headless GPU environments
Required experience
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Strong background in computer vision — camera models, homography, PnP, pose estimation
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Hands-on experience with Gaussian splatting pipelines (3DGS, 4DGS, or equivalent) — not just academic familiarity
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Proficient in Python and C++/CUDA — writing and optimising custom CUDA kernels, not just calling library APIs
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Deep familiarity with the NVIDIA stack: CUDA, cuDNN, TensorRT, NCCL, and associated tooling (Nsight, nvprof)
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Experience with Linux-native GPU workflows — driver management, CUDA environment configuration, headless rendering (EGL/OSMesa), containerisation with GPU passthrough (Docker + nvidia-container-toolkit)
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Experience optimising inference pipelines for hard real-time constraints
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Proficient with PyTorch including custom autograd, torch.compile, and mixed-precision workflows
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Comfortable working entirely in Linux environments — no Windows/Mac fallback
Highly desirable
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Experience with Unity 3D or Unreal Engine for real-time rendering and compositing — particularly headless or server-side rendering on Linux
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Background in broadcast or live production environments
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Experience with synthetic training data generation — perspective simulation, image augmentation, annotation pipelines
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Familiarity with object detection / segmentation models and their limitations at broadcast frame rates
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Experience with video streaming and low-latency pipelines — GStreamer, FFmpeg, or similar, ideally with hardware-accelerated encode/decode (NVENC/NVDEC)
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Profiling and tuning GPU memory bandwidth — understanding occupancy, warp divergence, and memory coalescing at a practical level
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Experience with multi-GPU setups and pipeline parallelism
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Background in sports broadcast or live event production technology
You might be a fit if you've...
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Built or contributed to a Gaussian splatting capture-to-inference pipeline end-to-end
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Shipped something that runs on GPU in a hard real-time constraint
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Done any kind of scene reconstruction or AR overlay work in a live video context
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Worked on training data pipelines for vision models at scale
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Debugged a CUDA pipeline in a headless Linux environment under production pressure
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If you think you can do it better, we are listening…
What we're not looking for
Standard YOLO/feature-matching solutions and pixel-level replacement approaches are well-understood territory — we've specifically moved past them. We need someone who understands why geometry-based and neural rendering approaches outperform them in this context, not someone who needs convincing.
