Senior Machine Learning Engineer

Western Governors University•Raleigh, NC

About The Position

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 salary range for this position takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. At WGU, it is not typical for an individual to be hired at or near the top of the range for their position, and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is: Grade: Technical 411 Pay Range: $157,000.00 - $243,400.00 Job Description The Senior ML Engineer builds and deploys state-of-the-art NLP/LLM models at scale in a cloud environment, with a focus on improving student learning experiences. You lead by example, mentor junior engineers, and collaborate across cross-functional teams. You actively research the latest NLP/LLM techniques and translate them into practical, scalable solutions for the education domain. You communicate clearly with leadership and peers, influence product directions, and drive innovation that challenges the status quo.

Requirements

Master’s degree in Computer Science, Software Engineering, Data Science, Machine Learning, Mathematics, Physics, or a related field; or equivalent relevant experience.
5+ years of software development in a cloud environment.
3+ years building large-scale ML/DL models, from POC to production.
Hands-on experience with one or more DL frameworks (e.g., PyTorch, TensorFlow).
Experience with cloud data platforms (AWS, Azure, GCP) and data/ML tooling (e.g., Databricks, MLFlow, Streamlit).
Proficiency in ETL, feature engineering, data visualization.
Experience operating high-availability, fault-tolerant, scalable distributed systems with GitOps practices (Terraform preferred).
Familiarity with stream processing (ksqlDB, Spark Streaming, Beam/Flink) and modern ML deployment patterns.
Strong programming skills in Python, Java/Scala, and/or Go; fluency in clean, maintainable code.
Excellent analytical, critical thinking, and problem-solving abilities.
Effective written and verbal communication; comfortable explaining technical concepts to non-experts and senior leadership.
Ability to thrive in a fast-paced, collaborative environment.
Experience guiding junior engineers and providing technical leadership.

Nice To Haves

PhD in a related field.
Experience with Databricks and a broad range of ML tooling.
Equivalent relevant experience may substitute for degree requirements (1 year of experience per year of education at the discretion of the Hiring Manager).

Responsibilities

Define NLP initiatives, roadmaps, and success metrics in collaboration with the MLE manager.
Champion best practices in ML, data governance, and security within the team and across the organization.
Mentor junior engineers and serve as a technical lead on complex ML projects.
Research and prototype state-of-the-art NLP/LLM techniques; evaluate and select approaches suitable for production.
Develop, train, fine-tune, and optimize production-grade NLP/LLM models.
Deploy models to production with emphasis on performance, scalability, reliability, and observability.
Partner with Data Engineering to build robust data processing pipelines and high-quality training/inference data.
Work with MLOps to ensure scalable, reproducible deployment, monitoring, and model governance.
Collaborate with Software, Infrastructure, and Security teams to integrate ML solutions into the university ecosystem.
Translate business requirements into NLP capabilities; collaborate with product stakeholders to validate outcomes.
Apply NLP insights to unstructured data sources (e.g., transcripts, emails, mentor notes) to inform learning experiences.
Stay current with NLP/LLM, DL, and AI trends; proactively apply innovations to use cases.
Contribute to standards, guidelines, and documentation for ML practices.
Communicate status, risks, and progress to leadership and cross-functional teams.