ML Engineer | 5+ Years | Remote

We are seeking a talented and innovative Machine Learning Engineer to join our dynamic team. The successful candidate will be responsible for designing, implementing, and deploying machine learning models to solve complex problems. If you have a passion for cutting-edge technology and a strong background in machine learning algorithms, we want to hear from you.

• Collaborate with cross-functional teams to understand business requirements and identify opportunities for applying machine learning solutions.
• Design and develop machine learning models for various applications, such as predictive analytics, recommendation systems, and natural language processing.
• Clean, preprocess, and analyze large datasets to extract meaningful insights and features for model training.
• Implement and optimize machine learning algorithms and frameworks.
• Evaluate and fine-tune model performance using appropriate metrics.
• Stay current with advancements in machine learning and artificial intelligence to integrate new techniques into our solutions.
• Collaborate with software engineers to integrate machine learning models into production systems.
• Participate in code reviews and contribute to the development of best practices.

Requirements:

• Master's or Ph.D. in Computer Science, Machine Learning, Data Science, or a related field.
• Proven experience in developing and deploying machine learning models in real-world applications.
• Strong programming skills in languages such as Python or R.
• Proficiency in machine learning frameworks (TensorFlow, PyTorch, scikit-learn, etc.).
• Solid understanding of statistical concepts and data analysis techniques.
• Experience with big data technologies and distributed computing (e.g., Apache Spark).
• Excellent problem-solving and critical-thinking skills.
• Effective communication and collaboration skills.

Nice to have:

• Publications or contributions to the machine learning community.
• Experience with cloud platforms (AWS, Azure, Google Cloud).
• Knowledge of deep learning techniques and architectures.
• Familiarity with containerization and orchestration tools (Docker, Kubernetes).