AI Machine Learning R&D Engineer job opportunity at Welocalize.

Master's (M.Sc.)

Worldwide

summarize with Ai

The AI Machine Learning Research & Development Engineer 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, ML Flow, 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. __ At Welocalize, we embrace a flexible, delivery-focused culture where success is measured by results, not rigid schedules. Team members are trusted to manage their own time, as long as commitments and deliveries are met. __ The core hours are 4pm–6 pm CET, were our global team, from the west coast of US to India, is simultaneously online. Important cross-team meetings often take place in that time slot. __ Join our global AI MLE R&D team, collaborating with teams worldwide to drive innovation and excellence. As part of this central function, you will help design, develop, and implement cutting-edge AI solutions to serve our organization. The goal: foster a collaborative, innovative, and operationally excellent AI/ML engineering culture. __ MAIN DUTIES The following is a non-exhaustive list of responsibilities and areas of ownership of an AI/ML R&D Engineer Design and develop machine learning models and algorithms for various aspects of the localization and business workflow processes, including machine translation, LLM finetuning, and quality assurance __ Take ownership of key projects from definition to deployment, 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 Implement and optimize machine learning models and technologies using Python, TensorFlow, and other relevant tools and frameworks __ Perform statistical analysis and fine-tuning using test results Deploy machine learning models and algorithms using appropriate techniques and technologies, such as containerization using Docker and deployment to cloud infrastructure __ Use AWS technologies (including but not limited to #Sagemaker, #EC2, #S3) to deploy and monitor production environments Keep abreast of developments in the field, with a dedication to learning in the role Document diligently and communicate thoughtfully about ML experimentation, design, and deployment __ Project scope: Define and design solutions to machine learning problems. Integration with larger systems done with the guidance of more senior engineers. Success Indicators for a Machine Learning 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 action. Ethical and Responsible AI Development: Adherence to ethical AI practices, ensuring models are fair, unbiased, and responsible.