Computer Vision / Machine Learning Engineer (Contract)
Contract Length: 6 months
Work Arrangement: Hybrid (onsite 3 days/week in Greater Boston)
Computer Vision / Machine Learning Engineer to support an active, production-focused initiative. This role is centered on improving detection and tracking accuracy for object-counting systems operating in real-world, high-throughput environments.
The ideal candidate has hands-on experience building and deploying computer vision models, working with multimodal sensor data, and optimizing inference pipelines for edge deployment.
What You’ll Work On
- Enhancing object detection, segmentation, and tracking accuracy in operational systems
- Developing and deploying models into validation and pre-production environments
- Improving real-time performance and reliability on edge hardware
Key Responsibilities
- Model Development: Train, validate, and deploy computer vision models, with an emphasis on instance segmentation
- Tracking & Fusion: Implement tracking approaches that combine color and depth data to maintain object persistence across frames
- Data Quality: Support data curation efforts and audit external annotations to ensure high-quality ground truth
- Performance Optimization: Tune inference pipelines for low-latency execution on edge platforms
Technical Environment
- Computer Vision & ML: Instance Segmentation, Object Tracking
- Sensor Data: RGB + Depth (basic multi-sensor fusion)
- Edge & Optimization: NVIDIA-based edge hardware, TensorRT or similar acceleration tools
Qualifications
- Proven experience delivering production-grade ML or CV systems
- Strong software engineering fundamentals (version control, testing, CI/CD)
- Experience deploying models beyond experimentation into real environments
- Ability to meet strict accuracy and performance benchmarks
- Comfortable working within cloud-only data environments with controlled access policies
Nice to Have
- Experience optimizing models for edge or embedded systems
- Familiarity with real-time or near–real-time vision pipelines
- Background in industrial, robotics, or high-volume operational settings
