Quick overview
Job description
Responsibilities
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Lead, mentor, and grow a team of 6-8 engineers building recommendation services
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Partner with ML to productionize recommendation models and ensure low-latency, high-availability serving infrastructure
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Own the technical roadmap for the recommender platform, balancing new capabilities with reliability and performance improvements
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Drive architecture decisions for recommendation and search infrastructure
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Establish and maintain engineering standards for code quality, testing, observability, and incident response
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Collaborate with Product, Design, and cross-functional engineering teams to define and deliver product-facing recommendation features
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Manage hiring, performance reviews, career development, and team culture
What We're Looking For
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8+ years of software engineering experience, with 4+ years in an engineering management role
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Strong backend systems expertise – you've built or operated large-scale distributed systems in production
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Experience with recommendation systems, search ranking, personalization, or adjacent ML-serving infrastructure
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Proficiency in one or more backend languages (ideally Go)
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Familiarity with data processing architectures, feature stores, and model-serving technologies (e.g., Kafka, Spark, ElasticSearch, etc)
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Track record of hiring, developing, and retaining high-performing engineering teams
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Ability to communicate technical trade-offs clearly to both engineers and non-technical stakeholders
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Experience with ML frameworks (TensorFlow, PyTorch) or MLOps tooling (MLflow, Kubeflow, Airflow)
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Hands-on experience with cloud infrastructure (AWS, GCP, or Azure) and container orchestration (Kubernetes)
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Background in A/B testing and experimentation platforms
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Prior work at scale (millions of daily active users or equivalent throughput)
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