Senior Backend Engineering Manager, Recommendations

4 мес. назад

219k–263k USD / yearUSASeniorHybrid

machine learningdata engineeringrecommendation systemsinfrastructurelow-latency systems

Manage the engineering team responsible for the recommendation engine at Hinge to deliver scalable, fair, and personalized match-making systems.

Hinge is the dating app designed to be deleted In today's digital world, finding genuine relationships is tougher than ever. At Hinge, we’re on a mission to inspire intimate connection to create a less lonely world. We’re obsessed with understanding our users’ behaviors to help them find love, and our success is defined by one simple metric– setting up great dates. With millions of users across the globe, we’ve become the most trusted way to find a relationship, for all. About the Role   At Hinge, the recommendation engine is a central part of our product. Every interaction users have with each other on our app begins with the systems your team builds and owns. As the engineering manager of this team, you will help drive the strategy and execution behind the infrastructure and feature

Lead, mentor, and grow a team of 6-8 engineers building recommendation services
Partner with ML to productionize recommendation models and ensure low-latency, high-availability serving infrastructure
Own the technical roadmap for the recommender platform, balancing new capabilities with reliability and performance improvements
Drive architecture decisions for recommendation and search infrastructure
Establish and maintain engineering standards for code quality, testing, observability, and incident response
Collaborate with Product, Design, and cross-functional engineering teams to define and deliver product-facing recommendation features
Manage hiring, performance reviews, career development, and team culture

8+ years of software engineering experience, with 4+ years in an engineering management role
Strong backend systems expertise – you've built or operated large-scale distributed systems in production
Experience with recommendation systems, search ranking, personalization, or adjacent ML-serving infrastructure
Proficiency in one or more backend languages (ideally Go)
Familiarity with data processing architectures, feature stores, and model-serving technologies (e.g., Kafka, Spark, ElasticSearch, etc)
Track record of hiring, developing, and retaining high-performing engineering teams
Ability to communicate technical trade-offs clearly to both engineers and non-technical stakeholders
Experience with ML frameworks (TensorFlow, PyTorch) or MLOps tooling (MLflow, Kubeflow, Airflow)
Hands-on experience with cloud infrastructure (AWS, GCP, or Azure) and container orchestration (Kubernetes)
Background in A/B testing and experimentation platforms
Prior work at scale (millions of daily active users or equivalent throughput)