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Instructure is seeking a Senior Machine Learning Engineer to design and own learner models that power mastery and progression capabilities in their education platform. You will build machine learning pipelines for knowledge tracing and longitudinal modeling, working with learning scientists to define what these models should measure and with infrastructure teams on deployment and operations.
Key responsibilities include: designing learner models that measure mastery and progression; shaping the data foundation by identifying key signals and building datasets; translating mastery definitions into model targets and evaluation criteria; building estimation and scoring approaches for sparse, noisy, and evolving behavioral data; owning models in production including training pipelines, testing, versioning, and monitoring; and clearly explaining model behavior and limitations to cross-functional partners.
You will need six or more years of applied machine learning or ML engineering experience with shipped products, depth in sequence modeling, latent-variable modeling, temporal modeling, Bayesian methods, or calibration, strong Python and production engineering skills, and strong evaluation instincts around calibration, uncertainty, stability, fairness, interpretability, and validation.
Bonus experience includes recommender systems, user-state modeling, personalization at scale, knowledge tracing, psychometrics, educational measurement, adaptive learning systems, structured knowledge representations (skills, standards, concept graphs), and experiment design or observational validation strategies.
The role requires onsite collaboration on Tuesday and Wednesday, with Thursday strongly encouraged. Instructure offers competitive compensation with ownership program participation, flexible work culture, generous time off including an annual recharge period in December, comprehensive wellness and mental health support, learning and development resources, and a culture rooted in inclusivity and meaningful connection.
