Computer Vision / Machine Learning Engineer

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