Senior Machine Learning Engineer, Ranking
New
J
JobgetherContent recommendations
Based in United States, Monday through Friday, from 9:00 a.m. to 3:00 p.m. Pacific TimeFull-TimeSenior
SalaryCompetitive compensation for candidates based in Canada: Toronto and Vancouver: CAD $243,330–$282,076, plus equity and benefits. Other Canadian locations: CAD $227,108–$263,271, plus equity and benefits.
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Job Details
- Experience
- At least 3 years of professional experience developing recommendation systems or ranking models. Preferred: At least 5 years of experience writing production-quality Python or C++ code.
- Required Skills
- PythonMachine LearningC++
Requirements
- Have at least 3 years of professional experience developing recommendation systems or ranking models.
- Understand the mathematical foundations of machine learning algorithms.
- Have experience designing, building, and deploying end-to-end machine learning systems in production.
- Hold a bachelor's, master's, or doctoral degree in Computer Science, Engineering, or a related technical discipline.
- Be available for meetings and real-time collaboration Monday through Friday, from 9:00 a.m. to 3:00 p.m. Pacific Time.
- Preferred: Have at least 5 years of experience writing production-quality Python or C++ code.
- Preferred: Have experience with generative recommendation systems and modern recommendation modeling techniques.
- Preferred: Be familiar with user behavior modeling, user sequence modeling, transformers, or long-sequence modeling applied to ranking.
- Preferred: Have experience leading large-scale technical projects involving multiple engineers.
Responsibilities
- Improve existing machine learning ranking models using software engineering skills and machine learning techniques.
- Own machine learning systems end to end, including data pipelines, candidate extraction, feature engineering, model training, evaluation, and production integration.
- Advance second-stage ranking models for personalized feeds and digest emails.
- Identify opportunities to apply machine learning across product experiences and improve content discovery.
- Collaborate with machine learning engineers to design, implement, and optimize algorithms and systems.
- Develop reusable methodologies and engineering practices for developers building on ranking systems.
- Experiment with modeling techniques, evaluate results, and improve system performance and reliability.
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