Machine Learning Engineer

Key Responsibilities

• Design, develop, and optimize machine learning models
• Collaborate with cross-functional teams (data scientists, product managers, software engineers) to integrate ML solutions into products
• Conduct experiments, analyze results, and implement improvements to enhance model performance
• Develop efficient data processing pipelines for model training and inference
• Optimize models for production deployment with attention to scalability and performance
• Create and maintain documentation for models, datasets, and processes
• Monitor model performance in production and implement updates to ensure reliability and quality
• Stay current with ML research and evaluate new techniques for potential implementation
• Participate in code reviews and contribute to best practices in ML engineering

Requirements

• Strong experience with ML frameworks and libraries such as PyTorch, scikit-learn, spaCy, and Hugging Face
• Practical understanding of the mathematics behind modern machine learning, linear algebra, and statistics
• Experience with cloud platforms (AWS, GCP, Azure) for ML deployment
• Familiarity with containerization technologies (Docker, Kubernetes)
• Experience with version control systems (Git) and CI/CD pipelines
• Proficiency with SQL and experience working with large datasets
• Excellent problem-solving and analytical thinking skills
• Strong communication skills with ability to explain complex technical concepts to non-technical stakeholders

Nice-to-Have skills:

• Experience with model versioning and experiment tracking tools (MLflow, DVC, Weights & Biases)
• Knowledge of data visualization tools (Matplotlib, Seaborn, Plotly)
• Experience with feature stores and ML platforms
• Understanding of ML monitoring and observability best practices