Engineering Manager, Ranking Platform

New

Reddit, Inc.Machine learning infrastructure

Remote - United StatesFull-TimeManager

Salary$230,000 — $322,000 USD

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Job Details

Experience: 5+ years of people management experience; 7+ years of experience building large-scale backend distributed systems.
Required Skills: People ManagementRESTful APIs

Have 5+ years of people management experience leading high-performing engineering teams, including hiring, coaching, career development, and performance management.
Have 7+ years of experience building large-scale backend distributed systems, including substantial experience developing and shipping production software.
Have experience leading teams or major initiatives in ML infrastructure, recommendation or ranking systems, indexing, or streaming and batch data pipelines.
Demonstrate technical judgment in high-throughput platform services and APIs, balancing reliability, latency, scalability, data freshness, and cost.
Have a track record of defining technical direction and delivering platform initiatives across multiple quarters and teams.
Have experience improving production operations through deployment automation, monitoring, system diagnosis, incident management, and reliability engineering.
Have organizational and communication skills and the ability to build consensus across engineering and product teams.
Have a BS in Computer Science, a similar technical field, or equivalent practical experience.
Experience with Go or Python, Kubernetes, Docker, AWS or GCP, Redis, Postgres, Memcached, Solr, Milvus, Apache Beam, or Flink is a plus.

Set the team’s direction and roadmap with senior engineers and partner teams.
Select, scope, and deliver initiatives for ranking coordination services, ML indexes, and data pipelines.
Hire, onboard, coach, and develop engineers; set expectations, provide feedback, and manage performance.
Manage multi-quarter platform investments, milestones, dependencies, risks, and priorities.
Drive incident response, blameless postmortems, observability, safe rollouts, and proactive system hardening.
Establish reliability and performance goals and plan for on-call work and operational requests.
Partner with Feed, Search, Ads, ML Platform, and infrastructure teams to align roadmaps and adoption.
Guide architectural improvements and engineering practices to improve efficiency, maintainability, and scalability.

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