Senior Machine Learning Engineer (AI)

Location: Budapest, Hungary; hybrid, with onsite collaboration required on Tuesday and Wednesday and Thursday strongly encouraged

Salary: HUF 1.5M–HUF 2.2M per month

Company

Builds intuitive education and personal development products, including LMS capabilities that help educators and students learn together.

What you will do

• Design and build learner models, including knowledge tracing and longitudinal approaches, for mastery and progression features.
• Define relevant behavioral signals and build the datasets required for learner modeling.
• Translate mastery and progression concepts into model targets and evaluation criteria with learning scientists and product partners.
• Develop estimation and scoring methods for sparse, noisy, and evolving behavioral data.
• Own training and scoring pipelines, testing, versioning, and production monitoring.
• Explain model behavior, assumptions, and limitations to product, engineering, and learning partners.

Requirements

• Six or more years of experience in applied machine learning, machine learning engineering, or applied research, including ownership of models shipped into real products.
• Depth in sequence modeling, latent-variable or probabilistic modeling, temporal modeling, Bayesian methods, or model-output calibration.
• Strong Python and production engineering skills, including pipelines that train and score models and serve predictions to users.
• Strong evaluation skills covering calibration, uncertainty, stability, fairness, interpretability, and validation strategy.
• Ability to work onsite in Budapest on Tuesday and Wednesday; Thursday is strongly encouraged.

Nice to have

• 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 models reflect reality.

Culture & Benefits

• Research-driven, collaborative environment focused on applying AI to education.
• Full-time employees participate in an ownership program.
• Flexible work culture with remote, hybrid, and in-office arrangements varying by role and location.
• Generous time off, local holidays, and an annual late-December recharge period.
• Wellness programs, mental health support, professional development resources, and tuition reimbursement.
• Mentorship, hack weeks, internal conferences, and an inclusive culture.

Hiring process

• Background check required for all employees.
• Identity verification and confirmation of legal name, current physical location, contact number, and residential address may be required.