Edtech.com's Summary

WGU Corporation is hiring a Senior Machine Learning Engineer. This role involves building and deploying advanced NLP/LLM models at scale within a cloud environment to enhance student learning experiences, leading technical teams, mentoring junior engineers, collaborating across departments, and driving NLP innovation tailored to education.

Highlights

Lead strategic NLP initiatives, define roadmaps, and establish success metrics with management.
Mentor junior engineers and provide technical leadership on complex machine learning projects.
Research, develop, train, fine-tune, and deploy NLP/LLM models focusing on performance, scalability, and reliability.
Collaborate with Data Engineering for data pipelines, MLOps for deployment and monitoring, and other teams for integration within university systems.
Translate business requirements into NLP solutions and apply insights to unstructured educational data.
Maintain current knowledge of NLP, LLM, deep learning, and AI trends and apply innovations accordingly.
Master’s degree in CS, Software Engineering, Data Science, Machine Learning, Mathematics, Physics, or related field, or equivalent experience.
Minimum 5 years of cloud software development and 3 years in large-scale ML/DL model building from proof of concept to production.
Proficient in DL frameworks such as PyTorch or TensorFlow; experience with AWS, Azure, or GCP and ML tools like Databricks, MLFlow, Streamlit.
Strong skills in Python, Java/Scala, or Go and experience with GitOps practices (Terraform preferred), stream processing (ksqlDB, Spark Streaming, Beam/Flink), ETL, feature engineering, and data visualization.
Salary range: $157,000 - $243,400 with bonuses and comprehensive benefits including medical, dental, vision, retirement plans, paid time off, and parental leave.

Senior Machine Learning Engineer Full Description

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 411Pay 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.

Key Responsibilities

Strategic leadership
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.
Model research, development, and deployment
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.
Data, pipelines, and collaboration
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.
Product impact and stakeholder engagement
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.
Continuous improvement and learning
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.

Minimum Qualifications:

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.

Preferred Qualifications

PhD in a related field.
Experience with Databricks and a broad range of ML tooling.

Equivalents and Substitutions

Equivalent relevant experience may substitute for degree requirements (1 year of experience per year of education at the discretion of the Hiring Manager).

Position & Application Details
Full-Time Regular Positions (classified as regular and working 40 standard weekly hours): This is a full-time, regular position (classified for 40 standard weekly hours) that is eligible for bonuses; medical, dental, vision, telehealth and mental healthcare; health savings account and flexible spending account; basic and voluntary life insurance; disability coverage; accident, critical illness and hospital indemnity supplemental coverages; legal and identity theft coverage; retirement savings plan; wellbeing program; discounted WGU tuition; and flexible paid time off for rest and relaxation with no need for accrual, flexible paid sick time with no need for accrual, 11 paid holidays, and other paid leaves, including up to 12 weeks of parental leave.

How to Apply: If interested, an application will need to be submitted online. Internal WGU employees will need to apply through the internal job board in Workday.

Additional Information

Disclaimer: The job posting highlights the most critical responsibilities and requirements of the job. It's not all-inclusive.

Accommodations: Applicants with disabilities who require assistance or accommodation during the application or interview process should contact our Talent Acquisition team at recruiting@wgu.edu.

Equal Employment Opportunity: All qualified applicants will receive consideration for employment without regard to any protected characteristic as required by law.

Original Job Description

Key Responsibilities

Strategic leadership
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.
Model research, development, and deployment
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.
Data, pipelines, and collaboration
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.
Product impact and stakeholder engagement
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.
Continuous improvement and learning
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.

Minimum Qualifications:

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.

Preferred Qualifications

PhD in a related field.
Experience with Databricks and a broad range of ML tooling.

Equivalents and Substitutions

Equivalent relevant experience may substitute for degree requirements (1 year of experience per year of education at the discretion of the Hiring Manager).

How to Apply: If interested, an application will need to be submitted online. Internal WGU employees will need to apply through the internal job board in Workday.

Additional Information

Disclaimer: The job posting highlights the most critical responsibilities and requirements of the job. It's not all-inclusive.

Accommodations: Applicants with disabilities who require assistance or accommodation during the application or interview process should contact our Talent Acquisition team at recruiting@wgu.edu.

Equal Employment Opportunity: All qualified applicants will receive consideration for employment without regard to any protected characteristic as required by law.

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