Senior Backend Engineering Manager, Recommendations
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 features that power our recommendations. You’ll work closely with machine learning engineers, product managers, data scientists, and data engineers to build systems that balance personalization, fairness, and user experience at scale, from low-latency match-serving pipelines to the candidate retrieval and ranking systems that determine who users see and when.
Our ability to provide good recommendations is central to achieving trust, engagement, and, most importantly, whether people can find who they’re looking for on Hinge.
Responsibilities
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
What We're Looking For
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)
Nice to Have