Senior Engineering Manager, Recommender Systems

Raya; United States; Remote; USD 180.000 - 240.000; Vollzeit

Benefits dieser Stelle

Equity based employment

Unlimited vacation

12 weeks paid parental leave

Annual travel stipend $1,000

Medical and dental coverage

Learning opportunities

Zusammenfassung

Raya is seeking an Engineering Manager for Recommender Systems to lead a backend team focused on retrieval and ranking infrastructure powering recommendations. You will collaborate with the head of ML to align tech strategy with a strong user-centric product experience.

The role requires hands-on leadership, deep production systems experience, and a passion for scalable, low-latency services that affect millions of users. Equity-based employment and generous parental leave are offered.

Qualifikationen

• 3+ years managing engineers with hiring and performance responsibilities.
• Experience leading production recommender systems at scale.
• Hands-on with high-throughput distributed systems serving millions.
• Expertise in Go or Java/JVM, plus Postgres and cloud (AWS/GCP) and Kubernetes.

Aufgaben

• 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 deep product intuition and user empathy.
• Partner with the Machine Learning team to define roadmaps; balance user-facing improvements with infrastructure investments.
• Work with Data Science to co-own experimentation processes, tools, and best practices.

Kenntnisse

Go backend

Distributed systems

Production systems

Team leadership

Tools

PostgreSQL

AWS

GCP

Kubernetes

Elasticsearch/OpenSearch

Jobbeschreibung

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.

We offer comprehensive medical and dental coverage, $50 a day food delivery budget, equity based employment, a great culture, learning opportunities, unlimited vacation, 12 weeks paid parental leave, and we pay all employees $1,000 a year to go somewhere in the world that they’ve never been because of our values of human connection, empathy, and curiosity.

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. Catch regressions in latency, quality, or fairness before users notice.

• Create and execute a technical strategy that is grounded in deep product intuition and empathy for the end-user experience.

• Partner with the Machine Learning team to define roadmaps. Balance user-facing improvements with investments in infrastructure, reliability, and experimentation.

• Work with Data Science to co-own experimentation processes, tools, and best practices.

Qualifications

• 3+ years managing engineers, with direct responsibility for hiring, performance management, career development, and delivery.

• Experience leading or building production recommender systems at scale.

• Hands-on depth in high-throughput, low-latency distributed systems that serve millions of users.

• Expertise in Go, Java/JVM, or a similar backend language; Postgres, and AWS/GCP, and Kubernetes.

What Sets You Apart

• Experience with social networks or two-sided marketplaces in a subscription setting: you understand reciprocal interest, exposure, liquidity, and how great recommendations create long-term retentive value.

• 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.