Machine Learning Engineer

We are seeking an experienced Machine Learning Engineer to design, develop, and deploy machine learning models for real-world applications. The ideal candidate is passionate about AI, has strong software engineering skills, and can translate business or research requirements into production-ready solutions. This is a hands-on engineering role, where you’ll work on environment setup, scalable API design, and database architecture.

• Design, implement, and optimize machine learning models for production environments
• Collaborate with data engineers, software engineers, and product teams to integrate ML solutions
• Perform data preprocessing, feature engineering, and exploratory data analysis
• Develop and maintain ML pipelines, including model training, validation, and deployment
• Monitor model performance and implement improvements or retraining as needed
• Stay up-to-date with the latest ML research, techniques, and best practices
• Contribute to technical documentation and knowledge sharing

• Bachelor’s degree in a technical field
• Strong proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or scikit-learn
• Solid understanding of machine learning algorithms, statistics, and probability
• Experience with data preprocessing, feature engineering, and model evaluation
• Familiarity with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes) is a plus
• Ability to write clean, maintainable, and well-documented code
• Excellent problem-solving skills and ability to work independently or in a team
• Ability to work in a US time zone, Monday to Friday (8 hours per day)

• Master’s or PhD in Computer Science, Machine Learning, or a related field
• Experience in NLP, computer vision, recommendation systems, or reinforcement learning
• Exposure to MLOps tools and workflows

• Competitive pay, always in US dollars
• Work remotely from the comfort of your home
• Paid holidays and time off
• Performance bonus
• Global exposure to the world’s best companies

Originally posted on Himalayas