Company
Technology
SeniorMachineLearningEngineer,Recommendation
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Machine Learning Engineer, Recommendation. Skills: Recommendation systems, Machine Learning, Ranking models, Retrieval techniques. Design recommendation systems. Improve recommendation systems”
What You'll Achieve.
Measurable improvements in engagement; Measurable improvements in retention; Measurable improvements in satisfaction; Measurable improvements in content distribution; Enhance product performance; Enhance user experiences
Industry & Context.
Problem-solving ability
What They're Looking For.
Must Have
5+ years industry experience, Experience building production ML systems, Experience developing recommendation engines, Experience with retrieval and ranking architectures, Understanding of evaluation methodologies, Solid engineering fundamentals, Communication and collaboration skills
Nice to Have
Experience with semantic search, Experience with AI-powered recommendation systems, Experience with LLM-enhanced ranking, Experience with personalized content generation, Experience with multimodal machine learning, Familiarity with reinforcement learning, Familiarity with contextual bandits, Familiarity with explore/exploit strategies, Familiarity with long-term optimization frameworks, Familiarity with user-generated content ecosystems, Startup experience, Experience building ML systems from scratch
What You'll Do.
Design recommendation systems
Improve recommendation systems
Develop retrieval systems
Optimize ranking systems
Develop candidate generation pipelines
Develop embedding-based retrieval
Develop two-tower architectures
Develop ranking models
Develop serving infrastructure
Lead experimentation efforts
Improve models iteratively
Improve recommendation quality
Address cold-start users
Address creator discovery
Address evolving content ecosystems
Build user representations
Build creator representations
Build content representations
Build session representations
Use behavioral signals
Use contextual signals
Use engagement signals
Collaborate with Product teams
Collaborate with Data Science teams
Collaborate with Engineering teams
Define success metrics
Deliver measurable improvements
Develop production ML systems
Implement robust monitoring
Implement evaluation standards
Implement scalability standards
Implement reliability standards
Contribute to ML infrastructure evolution
Contribute to ML architecture evolution
Support AI-native discovery experiences
Translate user behavior patterns
Translate complex datasets
Enhance product performance
Enhance user experiences
Stay current with advancements
Research recommendation systems
Research ranking methodologies
Research retrieval techniques
Research AI personalization
How You'll Work.
Team & Collaboration
Product teams; Data Science teams; Engineering teams
Full Job Description
## Accountabilities Design, build, and continuously improve recommendation and search systems across content feeds, discovery experiences, search functionality, and content continuation features. Develop and optimize retrieval and ranking systems, including candidate generation pipelines, embedding-based retrieval, two-tower architectures, ranking models, and serving infrastructure. Lead end-to-end experimentation efforts, including hypothesis development, A/B testing, performance analysis, and iterative model improvements. Improve recommendation quality across key challenges such as cold-start users, newly created content, creator discovery, and rapidly evolving content ecosystems. Build user, creator, content, and session-level representations using behavioral, contextual, and engagement signals. Collaborate closely with Product, Data Science, and Engineering teams to define success metrics and deliver measurable improvements in engagement, retention, satisfaction, and content distribution. Develop production-grade machine learning systems with robust monitoring, evaluation, scalability, and reliability standards. Contribute to the evolution of long-term machine learning infrastructure and architecture supporting AI-native content discovery experiences. Translate user behavior patterns and complex datasets into actionable machine learning solutions that enhance product performance and user experiences. Stay current with emerging advancements in recommendation systems, ranking methodologies, retrieval techniques, and AI-powered personalization technologies. Requirements 5+ years of industry experience building and deploying production machine learning systems with significant ownership and technical leadership responsibilities. Proven experience developing recommendation engines, search systems, ranking models, feed optimization systems, advertising ranking platforms, or content discovery solutions. Strong background working on consumer-facing applications, particu
Applying for this Senior Machine Learning Engineer, Recommendation role?
Most applicants get filtered before a human reads their resume. See if yours makes the cut.
How to Apply on Lever
- Lever uses a streamlined one-page form — apply in under 5 minutes.
- LinkedIn import works well; review parsed data before submitting.
- The cover letter field is optional but visible to reviewers — use it to differentiate.
- Referral codes from employees can significantly boost visibility of your application.
ANONYMOUS · UNFILTERED
What do employees actually say about this company?
Real rants from real employees. Read before you apply.