Sr. Machine Learning Engineer - Computer Vision
New
United StatesFull-TimeSenior
Salary150,000 - 185,000 USD per year
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Job Details
- Experience
- 5+ years
- Required Skills
- AWSPythonImage ProcessingMachine LearningPyTorchTensorflowComputer Vision
Requirements
- Bachelor’s degree in Computer Science, Engineering, or related technical field, or equivalent practical experience.
- 5+ years of experience in machine learning engineering or applied computer vision roles.
- Strong Python programming skills with experience building production-grade ML systems.
- Hands-on expertise with deep learning frameworks such as PyTorch or TensorFlow.
- Strong background in computer vision, including CNNs, Vision Transformers, image processing, and feature extraction.
- Experience working with large-scale image datasets, including data cleaning, labeling strategies, augmentation, and dataset QA.
- Proven ability to deploy ML models into production and build scalable training and inference pipelines.
- Strong understanding of model performance tradeoffs (precision, recall, false positives/negatives, robustness).
- Experience working in noisy, imbalanced, or adversarial data environments.
- Excellent communication and collaboration skills across technical and non-technical stakeholders.
- Experience in biometric authentication, liveness detection, or fraud detection systems is highly preferred.
Responsibilities
- Design, build, train, and optimize computer vision and deep learning models for image classification, face liveness detection, and anti-spoofing (PAD) systems.
- Develop robust ML solutions for identity verification challenges, including deepfakes, replay attacks, synthetic media, and other adversarial threats.
- Build end-to-end ML pipelines covering data ingestion, preprocessing, labeling, augmentation, training, evaluation, and production deployment.
- Define and implement evaluation metrics balancing fraud detection performance, false acceptance/rejection rates, and real-world business impact.
- Conduct experimentation using techniques such as architecture tuning, hard-negative mining, and data balancing to improve model robustness and accuracy.
- Deploy and maintain production-grade ML services on cloud infrastructure (AWS), ensuring scalability, reliability, and observability.
- Collaborate with cross-functional teams (Product, Fraud, Engineering, Platform) to align ML solutions with security, compliance, and business requirements.
- Research and integrate emerging advances in computer vision, biometric authentication, and adversarial ML to strengthen fraud detection systems.
- Support code reviews, model reviews, and knowledge sharing to elevate engineering standards across the team.
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