Machine Learning Engineer

The Opportunity

Position :

Experience :

Location :

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Key Responsibilities

• Model Design & Optimization : Design, train, and rigorously optimize various machine learning models to achieve high performance and accuracy.
• Vision & Text Model Fine-tuning : Possess a deep understanding of, and ability to, fine-tune both Computer Vision (CV) and Natural Language Processing (NLP) models for specific tasks and datasets.
• Research-to-Production Implementation : Implement State-of-the-Art (SOTA) techniques from academic and industry research papers, translating them into robust, production-ready code and solutions.
• Architectural Expertise : Demonstrate hands-on experience with transformer architectures and Convolutional Neural Networks (CNNs).
• Efficient Python Development : Write efficient, scalable, and maintainable Python code for all stages of the ML lifecycle, from data preprocessing to model deployment.
• Cross-functional Collaboration : Work effectively in a cross-functional environment, collaborating with data scientists, software engineers, and product managers to integrate ML solutions into larger systems.
  

Skills And Qualifications

• Experience : Minimum of 3+ years of hands-on experience in training and deploying machine learning models, specifically in Computer Vision (CV) and Natural Language Processing (NLP).
• Python Proficiency : Strong and demonstrated proficiency in Python programming, including experience with relevant ML/DL libraries (e.g., TensorFlow, PyTorch, Keras, scikit-learn).
• Model Architectures : Familiarity and practical experience with State-of-the-Art (SOTA) architectures in both CV and NLP, including various transformer models and advanced CNNs.
• Problem-Solving : Excellent analytical and problem-solving skills.
• Communication : Good communication skills, both written and verbal, for effective collaboration.
  

Desired Skills And Experience

• Core ML : Machine Learning principles and algorithms
• Domains : Computer Vision, Natural Language Processing
• Architectures : Convolutional Neural Networks (CNNs), Transformer models
• Programming : Python (efficient, scalable coding)
• Deployment : Experience transitioning models from research to production
• Collaboration : Cross-functional teamwork