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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and optimizing machine learning models for content ranking, with a strong focus on recommender systems and technical leadership. Proven ability to mentor teams, drive innovation through A/B testing, and apply cutting-edge AI techniques in real-time production environments.
Highest-signal resume keywords
Machine Learning EngineeringRecommender ModelingA/B TestingTechnical LeadershipMentorship
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningRecommender SystemsTransformer ArchitecturesDNN RankersGenerative ModelsLLM-native RecommendersHigh-QPS ML SystemsTraining InfrastructureCode ReviewsApplied Research
Soft Skills
Outstanding Communication SkillsCollaboration
Tools & Technologies
AI ToolsEvaluation FrameworksMonitoring Systems
Certifications & Qualifications
Graduate Degree in Computer Science
Industry Keywords
Content PersonalizationContent QualityCold-startLow-signal UsersTechnical Direction
About the role
Key responsibilities & impact- Build the most complex machine learning models and systems for notification content ranking
- Lead multiple technical sub-areas within Growth Content Science
- Drive ML model improvements through an A/B-test-driven innovation loop from ideation to production launches
- Own training pipelines, feature and serving infrastructure, evaluation frameworks, monitoring, and A/B test setup
- Raise technical excellence through mentorship, reviews, evaluations, documentation, and standard setting
- Collaborate with colleagues and stakeholders on cross-functional initiatives
- Work with engineering leaders on technical directions and roadmaps
- Operate autonomously and own complex, evolving work
- Apply state-of-the-art techniques from recent research and collaborate on novel research directions
- Optimize work using cutting-edge AI tools
Requirements
What you’ll need- Graduate degree in Computer Science or a related field
- Minimum of 8 years of experience in machine learning engineering
- Hands-on experience building and shipping models for a large-scale, real-time, end-user production recommender system
- Expertise in recommender modeling, including sequence modeling with transformer architectures, DNN rankers, and generative and LLM-native recommenders
- Experience building and operating high-QPS, latency-constrained ML systems, including training and serving infrastructure
- Experience owning cost, latency, and reliability tradeoffs
- Industry-grade software engineering experience
- Experience conducting thorough code and design reviews
- Understanding of content personalization, content quality, cold-start, and low-signal users
- Experience leading technical work across teams and making decisions on difficult tradeoffs
- Experience mentoring junior engineers
- Outstanding written and verbal communication skills
- Demonstrated experience using AI to improve speed and quality in applied research
- Strong track record of publications and patents
- Must be based in the United States; able to attend the office 1-2 times per quarter
Benefits
Comp & perks- Equity eligibility
- Flexible work model
- In-person collaboration 1-2 times per quarter
- Equal opportunity employment and accommodation support
- No relocation assistance (stated as an eligibility limitation, not a benefit)