Machine Learning Engineer
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Job Description
Machine Learning Engineer for Weekday's client solving complex visual intelligence problems. Designing and deploying image-focused ML solutions while collaborating with cross-functional teams.
Responsibilities:
- Design, develop, and deploy machine learning models for image-based tasks such as classification, object detection, segmentation, super-resolution, and image generation
- Partner with cross-functional teams to define imaging use cases, translate requirements into technical solutions, and deliver end-to-end ML workflows
- Build and maintain robust image preprocessing, augmentation, and data validation pipelines for diverse datasets
- Implement, train, and fine-tune deep learning architectures including CNNs, vision transformers, diffusion models, and other modern vision frameworks
- Evaluate model performance using quantitative metrics and visual analysis to identify failure modes and improvement opportunities
- Optimize models for scalability, latency, and real-time inference through techniques such as quantization, pruning, and efficient architecture design
- Contribute to production-grade ML pipelines, including model versioning, deployment, monitoring, and MLOps best practices
- Stay current with the latest research in computer vision and apply innovative approaches to solve business-critical challenges
Requirements:
- 3+ years of hands-on industry experience in machine learning and deep learning with a strong focus on computer vision
- Solid understanding of core vision concepts such as convolutional networks, feature extraction, image transformations, and geometric reasoning
- Strong proficiency in Python and deep learning frameworks including PyTorch or TensorFlow, along with tools like OpenCV and scikit-learn
- Experience training and tuning large-scale models on GPU-based infrastructure
- Strong grasp of model evaluation techniques and image quality metrics such as IoU, PSNR, and SSIM
- Hands-on exposure to deploying ML models in production environments using Docker and modern MLOps practices
- A curious, research-driven mindset with the ability to translate new ideas into practical, high-impact solutions
- Bonus experience with transformer-based vision models, multimodal learning, synthetic data generation, or edge/embedded vision systems
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