Machine Learning R&D Engineer

As a trusted global transformation partner, Welocalize accelerates the global business journey by enabling brands and companies to reach, engage, and grow international audiences. Welocalize delivers multilingual content transformation services in translation, localization, and adaptation for over languages with a growing network of over , in-country linguistic resources. Driving innovation in language services, Welocalize delivers high-quality training data transformation solutions for NLP-enabled machine learning by blending technology and human intelligence to collect, annotate, and evaluate all content types. Our team works across locations in North America, Europe, and Asia serving our global clients in the markets that matter to them. To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.Job Reference: #LI-JC1 Role Summary: The Machine Learning R&D Engineer role is responsible for the design, development and implementation of machine learning solutions to serve our organization. This includes ownership or oversight of projects from conception to deployment with appropriate AWS services, Docker, MLFlow, and other. The role also includes responsibility for following best practices with which to optimize and measure the performance of our models and algorithms against business goals.

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

Essentials 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

Bachelor’s degree in Computer Science, AI/ML, or related field (Master’s/PhD preferred). 6+ years of experience in AI/ML research and development. Expertise in machine learning frameworks and algorithms. Advanced programming skills in Python. Expertise in coding. Hands-on experience in NLP. Experience with deploying models in production environments. Leadership and mentorship experience, with excellent communication skills.

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