Senior AI Machine Learning Engineer

Corporate – Technology /
Full Time /
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.

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.


The Senior AI Machine Learning Engineer role carries responsibility for the discovery, design, development and implementation leadership of machine learning solutions to improve our corporate services. This includes ownership or oversight of projects from conception to deployment with ML deployment techniques and technologies, specifically the appropriate AWS services, Docker, MLflow, and other tools.
The role involves close collaboration with our team of developers, data engineers, product managers, business process experts and stakeholders to build models and systems that automate and optimize various aspects of our localization and business processes. The role also includes responsibility for establishing best practices with which to optimize and measure the performance of our models and algorithms against business goals.
The Senior AI Machine Learning Engineer provides guidance and support to all members of the AI ML team. Additionally, the role requires effective communication with a diversity of business stakeholders who may have little to no machine-learning knowledge.


    • The following is a non-exhaustive list of responsibilities and areas of ownership of the Senior AI Machine Learning Engineer:
    • Design and develop machine learning models and algorithms for various aspects of the localization and business workflow processes, including business intelligence, machine translation, LLM finetuning, and quality assurance
    • Collaborate with developers, data engineers, and business process stakeholders to gather requirements and identify areas for improvement
    • Take ownership of key projects from conception to deployment, ensuring that they meet business requirements and maintain momentum and direction until delivery
    • Provide oversight of the discovery phases, data specification and scientific direction of projects owned by ML engineers
    • Evaluate and select appropriate machine-learning techniques and algorithms to solve specific problems
    • Develop tools and metrics to measure the performance of our models and systems
    • Deploy machine learning models into a large-scale AWS environment, including architecture design and deep knowledge of AWS best practices
    • Communicate effectively with all business stakeholders who may have little to no machine learning knowledge, explaining and exploring the benefits and limitations of various machine learning approaches in the context of their business goals
    • Deploy machine learning models and algorithms using appropriate techniques and technologies, such as containerization using Docker and deployment to cloud infrastructure
    • Perform statistical analysis and fine-tuning using test results
    • Implement and optimize machine learning models and technologies using Python, Tensor Flow, Pandas, and other relevant tools and frameworks
    • Provide guidance and support to members of the team, mentoring in best practices for machine learning management approaches, management environment, development and deployment 
    • Keep abreast of developments in the field, with a dedication to learning in the role


    • Education 
    • Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field
    • Experience
    • 5+ years of experience in developing and implementing machine learning models and algorithms
    • Knowledge & Skills
    • Strong knowledge of machine learning techniques and algorithms, including supervised and unsupervised learning, deep learning, and reinforcement learning
    • Hands-on, high proficiency experience with machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn
    • Experience with natural language processing (NLP) techniques and tools
    • Ability to write robust, production-grade code in Python
    • Significant experience with ML management technologies and deployment techniques. Must include enterprise experience with AWS and Docker. Examples of relevant (but not required) AWS services: Amazon Sagemaker, EC2, Lambdas, DynamoDB, RedShift.
    • Excellent problem-solving and analytical skills
    • Strong communication and collaboration skills, with the ability to explain complex technical concepts to non-technical stakeholders
    • Experience taking ownership of projects from conception to deployment, and mentoring more junior team members