Skills

Full description

Design and build learner models including knowledge tracing and longitudinal approaches
Shape the data foundation for learner modeling including 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 whether a model reflects 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

Senior Machine Learning Engineer, Learner Modeling

Compensation

Not disclosed

Location

Not listed

Work setup

Remote

Seniority

Senior

Employment

Full time

Experience

Not specified

Design and build learner models including knowledge tracing and longitudinal approaches
Shape the data foundation for learner modeling including 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 whether a model reflects 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