Vacancy description
Robotics AI Engineer | Defence in Noida, भारत
Zoid technologies Pvt. Ltd.
Rs 6 - 8 lakhs p.a.
Noida, भारत
Role Name - Robotics AI Engineer
Experience- 2-3 years
Location - Sector 132, Noida
About ZOID
ZOID is a deep-tech R&D startup working on advanced technologies for the defence and aerospace sector. We build customised solutions for the Indian Military and UAV manufacturers, with our strength being hands-on expertise in AI, electronics, UAVs and robotics.
Current projects include:
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
Role Brief:
We are looking for an Robotics AI Engineer to handle AI data operations through field data collection, image annotation, dataset QC, and test-set analysis for UAV/defence applications. The role involves working with field teams during runway/track tests and maintaining accurate, traceable datasets and test logs.
Key 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)
Must Have:
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.
Good to Have:
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.