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 250 languages with a growing network of over 400,000 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. www.welocalize.com
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.
MAIN PURPOSE OF ROLE
The Machine Learning Engineer role is responsible for the design, development, and implementation of data-driven 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.
MAIN DUTIES
- The following is a non-exhaustive list of responsibilities and areas of ownership of the Machine Learning Engineer:
- Machine learning model implementation: Work on the development and deployment of machine learning models for localization and business workflow processes, including machine translation and quality assurance. Implement existing solutions and ensure that models meet performance metrics.
- Maintain code quality: Write well-documented, efficient, and clean Python code following best practices and under the guidance of senior engineers.
- Collaborate on machine learning projects: Collaborate with cross-functional teams to implement solutions based on defined designs. Assist in understanding business requirements and contribute to discussions with technical teams. Support clear communication of technical progress to team members.
- Model Implementation and Development: Contribute to developing models that are accurate, efficient, and aligned with project requirements by implementing existing designs and methods.
- Team Collaboration: Work closely team members, actively participating in project discussions and contributing to successful outcomes through effective teamwork.
- Commitment to Learning: Show dedication to continuous learning, staying updated with new techniques, and applying them under guidance to improve models and processes.
- Clear Communication: Support clear communication within the team by sharing findings, challenges, and insights in a way that ensures understanding among peers and stakeholders
- Take ownership of key projects from definition to delivery, ensuring that they meet technical requirements and maintain momentum and direction until delivery
- Evaluate and select appropriate machine-learning techniques and algorithms to solve specific problems
- Propose solutions and strategies to tackle business challenges
REQUIREMENTS
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Education
- Bachelor's or Master's degree in Computer Science, Mathematics, Machine Learning, Data Science or similar discipline (or equivalent experience)
- Technical Skills and Experience
- 3+ years experience as a Machine Learning Engineer or similar role
- Essentials
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Solid understanding of machine learning concepts: Knowledge of supervised and unsupervised learning, deep learning, and classification techniques. Eager to learn and build on foundational knowledge.
- Hands-on experience with natural language processing (NLP) techniques and tools.
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Proficiency in Python: Ability to write clean, well-structured code following best practices.
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Good communication and documentation skills: Ability to explain technical work clearly to peers and team members, with focus on communicating with non-technical stakeholders.
- Nice to have
- Experience using Large Language Models in production.
- Proficiency with machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn.
- Experience with AWS technologies including EC2, S3, and other deployment strategies. Experience with SNS, Sagemaker a plus.
- Knowledge of ML management technologies and deployment techniques, such as AWS ML offerings, Docker, GPU deployments, etc.
- Experience with data visualization tools like matplotlib, bokeh, d3.js
- Analytical mind and business acumen
- Ability to collaborate in an international environment and within a distributed team
