Machine Learning R&D Engineer

Tasks and Responsibilities:

• Machine learning model research and development: design, develop and deploy machine learning models for localization and business workflow processes, including machine translation and quality assurance. Utilize appropriate metrics to evaluate model performance and iterate accordingly.
• Ensure code quality, write robust, well-documented, and structured Python code.
• Define and design solutions to machine learning problems.
• Work closely with cross-functional teams to understand business requirements and design solutions that meet those needs.
• Explain complex technical concepts clearly to non-technical stakeholders.
• Mentorship: Guide junior team members and contribute to a collaborative team environment.

Success indicators of a Machine Learning R&D Engineer:

• Effective Model Development: success is evident when the models developed are accurate, efficient, and align with project requirements.
• Positive Team Collaboration: demonstrated ability to collaborate effectively with various teams and stakeholders, contributing positively to project outcomes.
• Continuous Learning and Improvement: a commitment to continuous learning and applying new techniques to improve existing models and processes.
• Clear Communication: ability to articulate findings, challenges, and insights to a range of stakeholders, ensuring understanding and appropriate.

Skills and Knowledge

• Excellent, in depth understanding of machine learning concepts and methodologies, including supervised and unsupervised learning, deep learning, classification.
• Hands-on experience with natural language processing (NLP) techniques and tools.
• Ability to write robust, production-grade code in Python.
• Excellent communication and documentation skills. Able to explain complex technical concepts to non-technical stakeholders.
• Experience taking ownership of projects from conception to deployment. Ability to transform business needs to solutions.

Nice to have:

• Experience using Large Language Models in production.
• High proficiency with machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn.
• Hands-on experience with AWS technologies including EC2, S3, and other deployment strategies. Experience with SNS, Sagemaker a plus.
• Experience with ML management technologies and deployment techniques, such as AWS ML offerings, Docker, GPU deployments, etc.

Education and Experience

• Master degree in Computer Science, Data Science, Engineering, Mathematics or similar field; PhD is a plus
• 6+ years of experience in AI/ML research and development.