Senior Machine Learning Engineer
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Job Description
Senior ML Engineer developing production computer-vision models for GBG’s digital identity verification technology. Deploying, evaluating, and improving AI systems while mentoring CVML engineers in an Agile environment.
Responsibilities:
- Design, implement, and optimize machine learning and computer vision models for product capabilities
- Research, evaluate, and apply CNNs, transformers, and vision-language models
- Implement and benchmark algorithms on large-scale datasets for accuracy and throughput
- Fine-tune large-scale models using LoRA and QLoRA
- Define, implement, and monitor evaluation metrics including precision, recall, ROC-AUC, and confusion matrices
- Analyze training, test, and production data to identify performance gaps and reliability risks
- Improve model accuracy, robustness, and system stability through data-driven enhancements
- Support end-to-end ML workflows spanning data preparation, training, deployment, monitoring, and iteration
- Contribute to CI/CD pipelines and production monitoring for reliable, reproducible, scalable model delivery
- Diagnose and resolve model performance regressions and production issues
- Mentor junior CVML engineers across ML project phases
- Participate in design reviews, technical discussions, knowledge sharing, and Agile ceremonies
- Suggest improvements to models, workflows, tools, and product features
- Collaborate with engineering, product, and data stakeholders
- Monitor emerging ML and computer vision trends and assess their real-world applicability
Requirements:
- Bachelor’s degree or higher in Computer Science, Electrical Engineering, or a related field, or equivalent experience
- Strong hands-on experience developing and deploying machine learning models in production environments
- Advanced understanding of supervised, unsupervised, and semi-supervised learning
- Expertise in classification, regression, clustering, and anomaly detection
- Experience with convolutional neural networks, recurrent neural networks, and transformer-based models
- Strong proficiency in Python and PyTorch
- Experience with object detection, image segmentation, and representation learning
- Experience with computer vision and scientific computing libraries such as OpenCV
- Familiarity with model deployment, monitoring, and CI/CD workflows
- Experience with large-scale datasets and performance-critical ML systems beneficial
- Prior experience mentoring or technically guiding ML engineers
- Exposure to production MLOps practices and model lifecycle management beneficial
- Ability to balance research-driven exploration with pragmatic, production-focused execution
Benefits:
- Equal opportunity employer committed to a diverse and inclusive workplace
- Reasonable adjustments to the interview process
- Benefits information available from the Talent Attraction team
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