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Senior Engineering Manager, Recommender Systems

Full Remote
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Lead the backend retrieval and ranking team behind recommendations at Raya. Build, coach, and retain a high-performing backend team while owning reliable, low-latency production systems. Requires 3+ years managing engineers and experience leading production recommender systems at scale.

Key Highlights
Lead backend retrieval and ranking infrastructure for recommendations
Own production reliability, observability, and incident response
Partner with ML and Data Science on roadmaps and experimentation
Requires production recommender systems experience at scale
Remote full-time role
Key Responsibilities
Build, coach, and retain a high-performing backend team that specializes in recommender systems.
Own reliable, low-latency production systems, observability, and incident response for recommendations.
Create and execute a technical strategy grounded in product intuition and user empathy.
Partner with the Machine Learning team to define roadmaps and balance improvements with infrastructure investments.
Work with Data Science to co-own experimentation processes, tools, and best practices.
Technical Skills Required
Go/Java Postgres AWS/GCP/Kubernetes
Benefits & Perks
Comprehensive medical and dental coverage
$50 a day food delivery budget
Equity based employment
Learning opportunities
Unlimited vacation
12 weeks paid parental leave
$1,000 a year to travel somewhere new
Nice to Have
Experience with social networks or two-sided marketplaces in a subscription setting
Familiarity with how machine learning and AI fit into modern recommender systems
Experience with retrieval infrastructure such as Elasticsearch/OpenSearch, vector search, or feature stores

Job Description

As Engineering Manager for Recommender Systems, you'll lead the team that builds the backend retrieval and ranking infrastructure behind recommendations at Raya. You'll partner closely with the head of ML to drive our tech strategy and ensure our members get the best experience possible on the core surface of our product.

We are looking for someone who is technically strong, has deep experience with production recommender systems, and has intuitive product-focused user empathy.

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