Senior Machine Learning Engineer - Mexico
Western Governors University
If you’re passionate about building a better future for individuals, communities, and our country—and you’re committed to working hard to play your part in building that future—consider WGU as the next step in your career.
Driven by a mission to expand access to higher education through online, competency-based degree programs, WGU is also committed to being a great place to work for a diverse workforce of student-focused professionals. The university has pioneered a new way to learn in the 21st century, one that has received praise from academic, industry, government, and media leaders. Whatever your role, working for WGU gives you a part to play in helping students graduate, creating a better tomorrow for themselves and their families.
The Senior Machine Learning Engineer builds Machine Learning models, particularly NLP and LLM, and executing large NLP/LLM models on a cloud environment at scale. This role is essential in experienced learning, advancing machine learning skillset and sharing knowledge with others. As a senior member of the team, the Sr. ML Engineer works well individually and in a team. You have a passion for Machine Learning and enjoy researching start-of-the-art NLP and LLM techniques and applying them to the education domain. You have initiative and follow through with your tasks, while communicating with peers on status. The Senior Machine Learning Engineer applies direct ML knowledge and skills to make a direct impact on improving student learning experiences at WGU. This role has the courage to challenge the current state and propose innovative ideas. As a great communicator, the Senior Manager Learning Engineer works with cross-functional teams.
Essential Functions and Responsibilities:
Works closely with the MLE manager to define NLP initiatives, roadmaps and strategies.
Collaborate with the product team and project stakeholders to convert business requirements into requisite NLP capabilities.
Develops, deploys, and optimizes state-of-the-art LLM models for diverse NLP applications.
Utilizes NLP/LLM techniques to discover valuable insights from unstructured data sources such as call transcripts, emails, mentor notes, etc.
Utilizes generative AI to build the next generation learning experience for our WGU students.
Executes the entire ML development lifecycle including model research, data processing, model training and fine-tuning, model experimenting and evaluation, model improvement, as well as model deployment.
Collaborates with the Data Engineer team to develop and implement the data processing pipeline to ensure high-quality input for model training and inference.
Collaborates with the MLOps team to deploy ML models to production environment, ensuring scalability, reliability, and performance.
Collaborates with the Software, Infrastructure, and Security teams to integrate ML soluctions seamlessly into WGU eco-system.
Stays up to date with the state-of-the-art technologies of LLM, NLP, and Deep Learning, and proactively apply them to our use cases to drive innovation of WGU.
Works with other team members to create standards and guidelines for ML.
Follows Agile, ML best practices and company’s processes.
Communicates status and updates with leadership, team members and other teams.
Mentors and provides guidance to junior team members.
Investigates trends and needs for ML models.
Performs other related duties as assigned.
Knowledge, Skill and Abilities:
Experience operating high-availability, fault-tolerant, scalable, distributed software/infrastructure in production utilizing GitOps practices (Terraform preferred).
Experience with existing MLOps frameworks (Databricks, Seldon, Sagemaker, DVC, etc.).
Strong background with either Scala (Java), Go, or Python programming experience.
Substantial experience operating big data infrastructure in a cloud-based ecosystem (AWS preferred).
Solid understanding of traffic management and networking concepts.
Experience with stream-processing systems (ksqlDB, Spark Streaming, Apache Beam/Flink, etc.).
Experience with software engineering standard methodologies (unit testing, code reviews, design document, continuous delivery).
Develop and deploy production-grade services, SDK’s, and data infrastructure emphasizing performance, scalability, and self-service.
Ability to conceptualize and articulate ideas clearly and concisely.
Entrepreneurial or intrapreneurial experience leading the creation of a new product & organization.
Organizational or Student Impact:
Works proactively; anticipates and prevents highly complex problems crossing disciplines.
Develops and establishes technical/business processes.
Will provide highly innovative solutions for extremely specialized, complex technical issues.
Fully understands and quantifies program risks with broad, significant impact.
Problem Solving & Decision Making:
Develops and accomplishes goals and objectives independently.
This individual builds, leads, and integrates multiple project teams and broad assignments, driving decisions and results.
Provides strategy and guidance to develop technical talent.
Sets and models high standards for effective interactions across groups.
Communication & Influence:
This individual communicates with experts within and outside the organization related to significant advancements specific to technology.
Works to influence others to accept and understand technical direction, new concepts, practices, and approaches. Requires ability to communicate and influence senior executive leadership regarding matters of strategic importance to the organization.
M.S. degree or higher in Computer Science, Software Engineering, Data Science, Machine Learning/Deep Learning, Math, Physics or any related field.
5+ years of industry experience in Software Development within cloud environment.
3+ year of industry experience in building large scale Machine Learning or Deep Learning models, carrying out the entire ML development lifecycle from POC to production release.
Deep understanding of NLP and LLM concepts, including language modeling, text classification, sentiment analysis, token embeddings, etc.
Proficient programming skills such as Python, R, SQL, etc.
Hands on experience with one or more deep learning frameworks like PyTorch, TensorFlow, Huggingface, etc.
Experience with leading cloud and data platforms such as AWS, Azure, Sagemaker, Databricks, etc.
Experience with data ETL, feature engineering, and visualization techniques.
Experience with open-source ML tools and APIs such as MLFlow, Streamlit, etc.
Excellent problem-solving abilities to analyze complex data and requirements towards practical solutions.
Excellent creative thinking skills to come up with new solutions and approaches.
Strong communication and collaboration capabilities, being able to work seamlessly with business stakeholders and cross-functional teams.
Comfortable working in a fast paced, highly collaborative, dynamic work environment.
Experience with Databricks preferred.
AWS cloud platform experience preferred.