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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Backend Engineering Manager, Recommendations - **Company:** Hinge Inc. - **Location:** New York, NY, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** A/B Testing, Airflow, Amazon Web Services, Microsoft Azure, Cloud Computing, Software Quality, Distributed Systems, Elasticsearch, Machine Learning, Recommender Systems, Tensorflow, Software Engineering, Software Systems, Data Processing, Pytorch, Apache Spark, Backend, Kubernetes, Apache Kafka, Build Tools, Machine Learning Operations - **Published:** September 26, 2026 - **Apply:** https://www.juju.com/job/16_e2af32e34 ## About the Role * 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 Nice to Have * 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) ## Description 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 ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [How We Built a Machine Learning-Based Recommendation System (And Survived to Tell the Tale)](https://www.wearedevelopers.com/videos/752-how-we-built-a-machine-learning-based-recommendation-system-and-survived-to-tell-the-tale) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models) ## Related Articles - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [From developer to manager – what does it take to become an engineering manager?](https://www.wearedevelopers.com/magazine/42-from-developer-to-manager-what-does-it-take-to-become-an-engineering-manager) - [7 Most Popular Web Developer Jobs in Europe](https://www.wearedevelopers.com/magazine/163-7-most-popular-web-developer-jobs-in-europe) - [Is Software Engineering Over-Saturated?](https://www.wearedevelopers.com/magazine/418-is-software-engineering-over-saturated) - [Should Tech Managers Be Developers First? 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