About MacV AI
At MacV AI, we build real-time Vision AI systems for industrial environments processing live video streams to understand what's happening on the ground and prevent costly or dangerous outcomes. We're a small, passionate team working together to leverage cutting-edge technology to help build safer workplaces.
About the role
We're looking for a Computer Vision Engineer (24 years experience) who has hands-on experience building and deploying production-grade vision systems.
You will work directly with the founders on core product systems across two main tracks:
- Industrial Safety Systems: Real-time object detection for safety use cases, low-latency inference, and alert generation on live video streams
- Retail Intelligence: People detection, multi-object tracking, cross-camera re-identification, and demographic estimation
What you'll do
- Design and deploy end-to-end computer vision systems for real-world environments
- Train, fine-tune, and optimize models for detection, tracking, re-identification, and feature extraction
- Build and optimize real-time inference pipelines (latency, throughput, GPU utilization)
- Work on multi-camera systems including synchronization and identity tracking across feeds
- Integrate models into backend systems (APIs, databases, alerting pipelines)
- Evaluate trade-offs between accuracy, speed, and cost in production environments
- Experiment with state-of-the-art approaches (e.g. VLMs) where relevant to real-time systems
Requirements
- 24 years of experience in a computer vision / ML engineering role
- Proven experience building/deploying CV pipelines in production (not just academic or side projects)
- Strong proficiency in Python and PyTorch (or TensorFlow)
- Hands-on experience with object detection, multi-object tracking, model optimization
- Experience building or contributing to real-time video pipelines
- Familiarity with backend systems (FastAPI/Django) and databases (PostgreSQL/MongoDB)
- Strong debugging skills in real-world scenarios (data issues, latency, deployment failures)
Strong Plus
- Experience with NVIDIA DeepStream, TensorRT, ONNX Runtime
- Work on multi-camera tracking / re-identification systems
- Experience deploying on edge devices (Jetson, GPU servers)
What we offer
- High ownership - you'll work on systems that go live with real customers
- Meaningful ESOPs (employee stock options) designed to reward early team members as the company scales
- Direct exposure to real-world deployment challenges
- Opportunity to grow into a senior / lead role quickly
Tech Stack
Python, PyTorch, FastAPI, PostgreSQL, MongoDB, YOLO, VLMs, NVIDIA DeepStream, AWS / Azure
