Computer Vision Tech Lead
First seen by Alion on Oct 27, 2026.
Sigmawave AI Pte. Ltd. is a Singapore-based AI infrastructure company and IMDA Pixel Startup Accelerator alumnus. We develop Terra, a Visual Synthetic Data platform generating photorealistic, annotated training data for AI in defence, security, infrastructure, and industrial sectors. We work with government agencies, GLCs and global technology leaders.
To strengthen our computer vision capability, we are hiring a Computer Vision Tech Lead. This role is central to taking our models from experiment to reliable deployment in live, multi-site environments.
Role SummaryThe Computer Vision Tech Lead leads our computer vision work as a hands-on tech lead, building, training and deploying models while guiding a small team of computer vision engineers. The main product focus is VMS, our Video Management System: a centralised platform for live video monitoring, playback and event management, with Vision AI operations to deploy, monitor and validate models across large, multi-site environments.
The role works closely with QA, project managers and clients, giving broad exposure to the corporate and software world. This role is based in Singapore, working on-site with hybrid work-from-home days.
Key Responsibilities1. Model Development & Evaluation
• Build, train and evaluate models for object detection, segmentation, tracking, classification and video analytics.
• Keep model work reproducible through experiment tracking, benchmark datasets and versioning.
• Set evaluation metrics tied to client acceptance criteria, and report clearly what a model can and cannot do.
2. Deployment & Optimisation
• Deploy and optimise models on edge devices and GPU servers, meeting throughput and latency targets.
• Export and accelerate models using ONNX, TensorRT, OpenVINO or equivalent, including quantisation.
• Monitor models in the field and feed field data back into retraining.
3. System Architecture & VMS Integration
• Architect the computer vision parts of our systems, including video ingestion, inference pipelines and VMS integration.
• Design how detections feed into event analytics and situational awareness for operators.
• Integrate cameras and video streams over ONVIF and RTSP across multi-site deployments.
4. Team Leadership & Delivery
• Lead and mentor a team of 2 to 5 computer vision engineers, setting technical direction and reviewing code and models.
• Plan and estimate computer vision work, and keep delivery on track against project milestones.
• Support QA, project managers and clients on demos and acceptance.
Required Qualifications• 5+ years in machine learning or software engineering, mostly computer vision, including 2+ years leading or mentoring engineers.
• At least one computer vision model taken to production and used by real users.
• Strong Python, PyTorch and OpenCV, plus a deployment toolchain such as ONNX, TensorRT or OpenVINO.
• Solid system architecture skills: video streams, inference services, Docker and Linux, and an understanding of how event data from many sources is combined into operational insight.
• Clear written and verbal communication with engineers, project managers and clients.
• Open to all applicants, but you must be able to work in Singapore, on-site with hybrid work-from-home days.
Preferred Qualifications• Degree in Computer Science, Computer Engineering, Electrical Engineering or a related discipline.
• Experience working with Synthetic Data Generators or Visual Synthetic Data.
• Participated in and delivered enterprise-grade or public sector projects.
• Experience with video management systems such as Milestone XProtect, Axis Camera Station etc.
• Multi-camera tracking, re-identification or video analytics at scale.
• NVIDIA Jetson, DeepStream or Triton.
• C++ for performance-critical inference code.
What We Offer• Ownership of computer vision for a live product, from model to deployment.
• Broad exposure to the corporate and software world through work with government agencies, GLCs and global technology leaders.
• A small, senior team where your technical decisions ship.
• Hybrid working: on-site in Singapore with work-from-home days.
• Competitive compensation and professional development support.
How to ApplyPlease send your resume and a short note on one computer vision system you took to production, what it did, and what you would do differently, to [email protected]. Include "Computer Vision Tech Lead Application" in the subject line.
Skills
Interface with engineers, Machine Learning, Object Recognition, Nvidia, Computer Engineering, Model Quantisation, Pose Estimation, Model Evaluation, Permit to Work, Low-Latency Connectivity, Model Training, Computer Vision, Deployment Plan, Model Checking, Synthetic Methods, Reproducibility, Object Detection, ONNX, operational knowledge, Computer Vision Technology