Senior Backend Engineering Manager, Recommendations
Requirements at a glance
Senior Backend Engineering Manager, Recommendations at Match Group is a full-time role based in New York, New York. The posting mentions 4+ years of experience. It does not mention visa sponsorship. Key skills include AWS, Azure, Google Cloud, and Kubernetes. Applications are submitted through Lever.
- Experience
- 4+ years (mentioned)
- Visa sponsorship
- Not mentioned in the posting
- Application platform
- Lever
Skills in this posting
- AWS
- Azure
- Google Cloud
- Kubernetes
- Elasticsearch
- Kafka
- Machine learning
- PyTorch
- TensorFlow
Extracted automatically from the job description. Confirm details in the full posting below.
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)