Staff ML Engineer - Monetization & Decision Systems

Quizlet; San Francisco (CA); On-site; USD 190,000 - 274,500; Full time

Benefits offered by this job

Competitive salary

20 vacation days

Health, dental, and vision insurance

Employer-sponsored 401k plan

Access to LinkedIn Learning

Job summary

A leading educational technology platform located in San Francisco is seeking a Staff Machine Learning Engineer to enhance their AI-powered tools. The role involves designing predictive models that drive learner engagement and optimizing decision-making processes within their product. Candidates should have over 8 years of ML experience, strong Python skills, and a proven history of integrating ML systems. This onsite position fosters collaboration while working towards impactful educational outcomes.

Qualifications

• 8+ years of applied ML or ML-heavy engineering experience, with a track record of shipping production models.
• Deep expertise in classical ML techniques such as boosted trees, GLMs, survival models.
• Strong engineering skills with Python and common ML frameworks.

Responsibilities

• Lead the design and development of predictive and prescriptive models across learner-facing decisions.
• Evaluate approaches connecting offline modeling metrics to online experimental outcomes.
• Define and negotiate clean integration boundaries with engineering teams.

Skills

Applied ML or ML-heavy engineering experience

Classical ML techniques

Python and ML frameworks

Integration of ML systems

Experimentation design

Education

8+ years of experience in ML

Tools

scikit-learn

PyTorch

XGBoost

LightGBM

Job description

A leading educational technology platform located in San Francisco is seeking a Staff Machine Learning Engineer to enhance their AI-powered tools. The role involves designing predictive models that drive learner engagement and optimizing decision-making processes within their product. Candidates should have over 8 years of ML experience, strong Python skills, and a proven history of integrating ML systems. This onsite position fosters collaboration while working towards impactful educational outcomes.