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.
A Staff Machine Learning Engineer plays a pivotal role in leading and architecting complex machine learning projects, often acting as a bridge between technical teams and senior management. They are responsible for designing advanced algorithms, mentoring junior engineers, and driving strategic decisions to effectively integrate AI solutions into business processes.
Tasks and Responsibilities
- Leads Project Development: Strategically leads the development of complex machine learning projects, ensuring they align with business objectives. This involves setting timelines, defining milestones, and coordinating with various teams.
- Architects ML Solutions: Expertly architects and designs advanced machine learning models and systems, considering scalability, efficiency, and integration with existing infrastructure.
- Mentors Junior Engineers: Actively mentors junior machine learning engineers, providing guidance and support to foster their professional growth and enhance team capabilities.
- Coordinates with Cross-Functional Teams: Regularly coordinates with cross-functional teams, including local project management teams, globally based business development teams, and other technical units, ensuring cohesive progress and alignment with broader company goals.
- Drives Innovation: Continuously drives innovation by researching and implementing cutting-edge techniques and technologies in machine learning and artificial intelligence.
- Manages Stakeholder Relationships: Effectively manages relationships with key stakeholders, including senior management, to communicate project progress, challenges, and outcomes.
- Ensures Ethical AI Practices: Vigilantly ensures that all AI models and practices adhere to ethical guidelines, promoting responsible and unbiased AI development.
- Evaluates Project Outcomes: Critically evaluates the outcomes of machine learning projects, assessing their impact on business goals and identifying areas for improvement.
- Presents Technical Insights: Articulately presents technical insights and project updates to both technical and non-technical audiences, ensuring clarity and understanding.
- Facilitates Knowledge Sharing: Fosters a culture of knowledge sharing and continuous learning within the team, organizing workshops, and training sessions as needed.
Success Indicators for a Staff Machine Learning Engineer
- Innovative and Effective Solutions: success is marked by the development of innovative machine learning solutions that significantly enhance business processes and outcomes.
- Strong Leadership and Mentorship: demonstrated ability to lead projects effectively and mentor junior team members, contributing to the overall strength and expertise of the team.
- Positive Stakeholder Engagement: effective engagement with stakeholders, ensuring their needs are met and their understanding of complex ML concepts is clear.
- Ethical and Responsible AI Development: upholding the highest standards of ethical AI practices, ensuring solutions are fair, unbiased, and responsible.
- Continuous Improvement and Learning: a commitment to continuous learning and improvement, both personally and within the team, keeping abreast of the latest developments in the field.
Skills and Knowledge
- Advanced Programming Skills: Proficiency in programming languages like Python, R, or Java, with a strong emphasis on writing clean, efficient, and scalable code.
- Expertise in Machine Learning Algorithms: Deep understanding of a wide range of machine learning techniques, including both classical algorithms and modern deep learning approaches.
- Strong Statistical Analysis and Modeling: Advanced knowledge in statistics and the ability to apply these concepts to model development and data analysis.
- Data Engineering Proficiency: Skills in managing and processing large datasets, including expertise in big data technologies like Hadoop, Spark, or Kafka.
- Deep Learning Frameworks: Proficiency in using deep learning frameworks such as TensorFlow, PyTorch, or Keras for building complex models.
- Cloud Computing and MLOps: Experience with cloud platforms like AWS, Azure, or Google Cloud, and knowledge of MLOps practices for efficient model deployment and maintenance.
- Natural Language Processing and Computer Vision: Skills in specialized areas like NLP and computer vision, depending on project requirements.
- Strong Problem-Solving Abilities: Advanced problem-solving skills to tackle complex challenges in machine learning projects.
- Leadership and Mentorship: Ability to lead project teams and mentor junior engineers, fostering a collaborative and innovative environment.
- Effective Communication: Excellent communication skills for articulating complex technical concepts to both technical and non-technical stakeholders.
- Project Management: Strong project management skills, capable of overseeing multiple projects and ensuring timely and successful delivery.
- Ethical AI Practices: Deep understanding of ethical AI principles, ensuring the development of fair, unbiased, and transparent machine learning models.
- Continuous Learning: Commitment to continuous learning and staying abreast of the latest advancements in AI and machine learning technologies.
Education and Experience
- BS in Computer Science, Mathematics or similar field.
- Master’s degree is a plus.
- 8+ years of experience in AI/ML Engineering or similar roles.
