Lead Machine Learning Engineer

Master’s or Bachelor’s degree in Computer Science, Machine Learning, or related field.
3+ years of experience in developing and deploying machine learning models.
Strong background in computer vision and image classification.
Proficiency in Python and machine learning frameworks (TensorFlow required, ONNX or PyTorch a plus).
Experience with containerized ML deployments (Docker, Kubernetes).
Familiarity with REST APIs and microservices architectures.
Knowledge of image processing techniques and computer vision algorithms.
Understanding of ML model versioning, monitoring, and maintenance.

Develop and enhance TensorFlow-based image classification models.
Design and implement training pipelines for model iteration and improvement.
Analyze model performance and implement techniques to improve accuracy and reduce false positives.
Maintain and optimize existing ML inference services deployed in Kubernetes.
Collaborate with Engineers to ensure seamless integration between machine learning components and API services.
Monitor model performance in production and implement strategies to handle data drift.
Work with labeled datasets from the review interface to improve model quality.
Develop data preprocessing pipelines for image normalization and augmentation.
Implement feedback loops utilizing corrections from subject matter experts and metrics.
Research state-of-the-art computer vision techniques applicable to image recognition.
Evaluate new approaches for improving identification accuracy and processing efficiency.
Prototype and implement new features leveraging machine learning capabilities.
Work with full-stack engineers to integrate ML components into the broader application.
Partner with subject matter experts to understand domain-specific challenges.
Document ML approaches, model versions, and performance metrics.

Experience with image recognition or similar identification systems.
Background in developing systems that incorporate human feedback for model improvement.
Familiarity with SQL or similar databases for storing model outputs and metadata.
Experience with CI/CD pipelines for ML model deployment.