Senior Machine Learning Engineer, Learner Modeling
Summary
Senior ML Engineer at Instructure building learner models that power mastery and progression in their LMS. Designs models, builds training/serving pipelines in Python, and owns production quality, partnering with learning scientists and infrastructure.
Our AI team is where a lot of that gets built: applying advanced AI to real problems in learning, and turning research into product capabilities that educators and students use every day.
We're looking for a Senior Machine Learning Engineer to build and own the learner models behind our mastery and progression capabilities. You'll design the models, build the pipelines that train and score them, and own their quality once they're running in production.
You'll partner with our learning scientists on what these models should measure, and with our infrastructure team on deployment and operations.
Why Join Us
Join us and help shape the future of education by turning cutting-edge AI into reliable product capabilities.
At Instructure, we're on a mission to help educators and students learn together, anytime, anywhere, and however works best. You'll join our research-driven team tackling education's biggest challenges with cutting-edge technology.
We value diversity, creativity, and passion, and invest in our teams through mentorship, hack weeks, internal conferences, and a culture where innovation thrives. Here, you'll have the chance to build the next generation of LMS features that make a real impact on students and teachers, and do it in a collaborative, supportive environment that encourages experimentation and growth.
What You'll Need
- Six or more years in applied machine learning, machine learning engineering, or applied research, with ownership of models shipped into real products
- Depth in at least one of: sequence modeling, latent-variable or probabilistic modeling, temporal modeling, Bayesian methods, or calibration of model outputs, applied to data that changes over time
- Strong Python and production engineering skills: you write the pipelines that train and score your models, and you've shipped models that run on a schedule and serve predictions to real users
- Strong evaluation instincts around calibration, uncertainty, stability, fairness, interpretability, and validation strategy
It Would Be a Bonus If You Had
- Experience with recommender systems, user-state modeling, or personalization at scale
- Experience with knowledge tracing, psychometrics, educational measurement, or adaptive learning systems
- Experience combining structured knowledge representations, such as skills, standards, or concept graphs, with learner models
- Experience designing experiments or observational validation strategies to test whether a model reflects reality
Onsite Collaboration Requirement: This role requires working onsite on Tuesday and Wednesday, with Thursday strongly encouraged as part of our company’s in-person collaboration model.