Senior Machine Learning Engineer, Learner Modeling
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Summary
Senior ML engineer at Instructure (the company behind Canvas LMS) designing and owning learner models such as knowledge tracing and mastery estimation. Day to day: defining signals, building datasets and Python training/scoring pipelines, and running monitored models in production on sparse, noisy behavioral data.
- Design and build learner models including knowledge tracing and longitudinal approaches
- Shape the data foundation for learner modeling by defining relevant signals and building datasets
- Translate mastery and progression definitions into model targets and evaluation criteria
- Build estimation and scoring approaches for sparse, noisy, and evolving behavioral data
- Own models in production including training and scoring pipelines, testing, versioning, and monitoring quality
- Explain model behavior, assumptions, and limitations to product, engineering, and learning partners
- 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 including writing pipelines that train and score models and shipping models that run on a schedule and serve predictions to real users
- Strong evaluation instincts around calibration, uncertainty, stability, fairness, interpretability, and validation strategy
- 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 model reflection of reality
- Competitive compensation
- Participation in ownership program for full-time employees
- Flexible work culture with remote, hybrid, and in-office collaboration spaces
- Generous time off including local holidays and annual "Dim the Lights" period
- Comprehensive wellness programs and mental health support
- Learning and development resources including professional development tools and tuition reimbursement
- Technology and tools needed to do best work
- Motivosity employee recognition program
- Culture rooted in inclusivity, support, and meaningful connection