Overview

This role is NOT suitable for candidates whose experience is primarily in LLMs, generative AI, prompt engineering, RAG, agentic workflows, or API integration

Responsibilities

• Own the optics testing end to end (FOV, focal length and depth-of-field calculations) for detecting 3 x 3 x 3 mm objects.
• Plan and conduct optics testing including camera & lens settings (focus, motion-blur budget, exposure, gain, FPS, binning, etc).
• Build and manage the dataset, annotation and QA/QC rules, data versioning, and backup (Raid Configuration).
• Analyse the type of datasets which should be collected for any specific task.
• Develop the detection & classification model for millimetre-scale objects at high resolution.
• Optimise models for real-time inference on the GPU servers such that multiple cameras can simultaneously operate on a single GPU without any accuracy loss.
• Implement object localisation and geo-referencing of detections using RTK/GNSS and frame timestamps and validate CEP in the field.
• Maintain a model registry with regression tests so no release degrades detection performance; run the false-positive review loop with operations staff.
• Analyse why false positives are occurring and how to eliminate them
• Recommend computation hardware (GPU, memory, storage, camera network cards) based on model and data-rate requirements.
• Apply AI methods to 3D photogrammetry data. Run segmentation, object detection and change detection, and extend the 3D processing pipeline where required. (Good to have)

Qualifications

• AI-based FOD detection software for naval air stations
• Reusable Off-board Missile Decoy for anti-ship missile defence
• GNSS-Denied Navigation Suite
• Aerial Intelligent Mapping Suite
• Vision-based Kinetic Strike Solution
• Swarming Solutions
• Fluency in Python.
• Strong computer vision fundamentals
• Knowledge of PyTorch & implementation of object detection at high resolution and small object scale (YOLO/DETR family or equivalent).
• Dataset engineering: annotation tooling (CVAT/Label Studio), QC methods, data versioning (DVC), experiment tracking (W&B/MLflow).
• TensorRT, CUDA, ONNX, INT8 quantisation, multi-GPU inference.
• Optics knowledge: FOV, IFOV, GSD, focal length, MTF, exposure & experience with machine-vision cameras and NIR imaging (Good to have)
• Object localisation and geo-referencing: pixel-to-ground mapping, CEP estimation, working with RTK/GNSS and time-synchronised frames. (Good to have)
• 3D data handling: point clouds and meshes (Open3D, PCL, PDAL or equivalent), knowledge of 3D deep learning models (PointNet/PointNet++, KPConv or similar) for segmentation and detection on 3D models. (Good to have)
• Linux, Git and CI; comfortable deploying and debugging on field hardware.
• Experience with multi-camera systems: synchronisation, overlapping fields of view, and stitching or fusing outputs from a camera array.
• Familiarity with GigE-Vision/GenICam camera SDKs and PoE camera networks, enough to debug frame drops and timing with the software team.
• Active learning or semi-automatic labelling to cut annotation effort on large field datasets.
• Exposure to anomaly detection or unsupervised methods for finding objects that were never in the training set.
• LiDAR or depth-sensor data handling, useful for cross-checking optical detections and for AIMS 3D work.
• Understanding of EO/IR sensor behaviour beyond NIR (SWIR/MWIR), for future sensor upgrades.
• Basic Docker and model-serving experience for reproducible field deployments