Senior Backend Engineering Manager, Recommendations

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.

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

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)

Prior work at scale (millions of daily active users or equivalent throughput)

Factors such as scope and responsibilities of the position, candidate's work experience, education/training, job-related skills, internal peer equity, as well as market and business considerations may influence base pay offered. This salary range is reflective of a position based in New York City. This salary will be subject to a geographic adjustment (according to a specific city and state), if an authorization is granted to work outside of the location listed in this posting.