Computer Vision Research Engineer

Jobtailor; Greater London; On-site; GBP 85,000 - 120,000; Full time

Job summary

Jobtailor is seeking a research scientist in Greater London to advance 3D reconstruction, spatial AI, and neural scene representations. You will design and evaluate novel algorithms, work with production teams, and benchmark large-scale experiments under realistic hardware constraints.

The role requires a PhD with a strong publication record, expert Python/PyTorch skills, and the ability to translate research into production-ready solutions, including potential open-source releases or production

Qualifications

• PhD in CV/ML or closely related field; exceptional candidates with industry experience are considered.
• Strong research track record in 3D computer vision (reconstruction, depth estimation, view synthesis, SfM, SLAM).
• Strong publication record at top-tier conferences (CVPR, ICCV, ECCV, SIGGRAPH, NeurIPS).
• Strong programming skills in Python and PyTorch.
• Evidence of translating research into shipped features, production pipelines, or open-source releases.
• Nice to have experience with COLMAP, C++, and CUDA, and awareness of on-device constraints.

Responsibilities

• Conduct original research in computer vision, ML, and spatial AI.
• Design, implement, and evaluate novel algorithms for 3D reconstruction and neural scene representations.
• Collaborate with researchers and engineers to transition ideas into product.
• Develop large-scale experiments and benchmarking frameworks.
• Publish findings at leading conferences (CVPR, ICCV, ECCV, SIGGRAPH).

Skills

Python

PyTorch

Research leadership

Publication track record

C++/CUDA

Education

PhD in Computer Vision / ML / CS

Tools

COLMAP

CUDA

Job description

Overview

Conduct original research in computer vision, machine learning, and spatial AI. Design, implement, and evaluate novel algorithms for 3D reconstruction, neural scene representations, 3D Gaussian Splatting, geometric deep learning, Structure-from-Motion (SfM), feed-forward models, and spatial foundation models. Collaborate with research scientists and engineers to transition promising ideas into future products. Develop large-scale experiments and benchmarking frameworks. Work directly with production realities, including real capture conditions, hardware constraints, and real customers. Publish research findings at leading conferences such as CVPR, ICCV, ECCV, or SIGGRAPH.

Responsibilities

• Conduct original research in computer vision, machine learning, and spatial AI.
• Design, implement, and evaluate novel algorithms for 3D reconstruction, neural scene representations, 3D Gaussian Splatting, geometric deep learning, Structure-from-Motion (SfM), feed-forward models, and spatial foundation models.
• Collaborate with research scientists and engineers to transition promising ideas into future products.
• Develop large-scale experiments and benchmarking frameworks.
• Work directly with production realities, including real capture conditions, hardware constraints, and real customers.
• Publish research findings at leading conferences such as CVPR, ICCV, ECCV, or SIGGRAPH.

Requirements

• PhD in Computer Vision, Robotics, Machine Learning, Computer Science, Mathematics, Physics, or a closely related field. Exceptionally strong candidates with equivalent experience in industry would be considered.
• A strong research track record in 3D computer vision: reconstruction, depth estimation, novel view synthesis, SfM, SLAM, or closely related areas.
• Strong publication record at top-tier conferences such as CVPR, ICCV, ECCV, SIGGRAPH, NeurIPS, etc.
• Strong programming skills in Python and experience in PyTorch.
• Evidence that you can carry an idea from research into something real; that may be a shipped feature, a production pipeline, a widely used open-source release, or similar.
• Ability to independently drive research projects from idea generation through experimentation and publication.
• Nice to Have
• Experience with classical reconstruction tooling (COLMAP etc).
• C++ or CUDA, and a feel for performance and on-device constraints.