Requirements

• Hold an MSc in computer science, machine learning, robotics, or a closely related field, with experience designing, implementing, and evaluating advanced AI-based systems.
• Hands-on experience developing localisation, scene matching, or 3D reconstruction systems, understanding reliability under real-world data and deployment constraints.
• Deep familiarity with geometric computer vision and deep learning approaches, including learned feature matching, place recognition, visual SLAM, VIO, neural 3D representations, and practical techniques like domain adaptation or model compression.
• Solid software engineering skills, including writing clean Python code and experience deploying AI software to production with testing, QA, and monitoring.
• Excellent communication skills for clear internal and external presentation of research findings.
• Passion for staying updated on current research and extending state-of-the-art approaches in geometric deep learning, SLAM, and 3D vision.

Nice to haves

• PhD in computer vision, machine learning, robotics, or related field, with publications in top-tier venues (e.g. CVPR, NeurIPS, ICLR, ICCV, ICRA, IROS, ECCV).
• Experience designing, evaluating, and delivering end-to-end AI systems on edge devices with constrained compute resources.
• Experience with simulators, emulators, or synthetic data generation pipelines for geometry or localisation tasks.
• Experience with Rust and/or C++ programming languages.

What you'll be doing

• Develop and benchmark scene matching and geo-registration pipelines robust to varying conditions such as illumination, viewpoint, season, or sensor changes.
• Build and evaluate SLAM, visual odometry, or structure-from-motion systems for deployable platforms considering real-world operational constraints.
• Research and adapt state-of-the-art geometric deep learning or feature matching methods for local feature descriptors and place recognition.
• Collaborate with product and deployment teams to integrate localisation and reconstruction outputs into navigation or decision-making pipelines and shape capability roadmaps.
• Develop computer vision models leveraging and extending state-of-the-art methods, design experiments, and conduct benchmarks to improve real-world performance and adapt solutions to hardware constraints.
• Contribute to impactful projects by collaborating across research, engineering, and product teams.

Perks and benefits

• Competitive salary and VSOP options.
• Relocation support up to €2,500 plus 4 weeks temporary accommodation.
• Annual learning allowance (€500 / £450).
• Health and wellness benefits including gym membership and mental health support (Nilo.health).
• Regular company events and monthly social allowances.
• Enhanced parental leave: 22 weeks fully paid for primary caregivers and 6 weeks for secondary caregivers.
• Family support including 5 days paid family emergency leave, 100% remote work option during pregnancy, and phased return to work.