Grindr runs a mobile social platform for gay users, and its stack shows ML at the core: PyTorch, TensorFlow and Spark on Databricks and Snowflake, deployed on Kubernetes across AWS and GCP. Hiring is accelerating and skews staff- and senior-level in product, engineering and data, with job posts concentrating on spam, fraud and fake-account detection alongside recommendation quality and legacy modernization.
Grindr is a publicly traded consumer technology company headquartered in Los Angeles, California, founded in 2009. It operates a location-based mobile app that helps gay users connect, with adjacent offerings spanning health, travel and advertising. The platform uses machine learning for relevance, personalization, safety and trust. Engineering runs on Java, Kotlin, Scala and Python, with data infrastructure built on Snowflake, Airflow, Kafka and Databricks. The company lists 51–200 employees and currently hires in the United States.
Grindr uses Python, Java, Kotlin and Scala, with PyTorch and TensorFlow for ML, Snowflake, Databricks, Spark, Airflow and Kafka for data, and Kubernetes, Terraform, AWS and GCP for infrastructure.
Yes. Grindr's hiring is accelerating, led by product, engineering, data and design roles, mostly at staff and senior level, all based in the United States.
Job posts point to recommendation system improvements, spam detection, AI-powered moderation, automated model deployment, authenticity and trust products, and a rewarded ads portfolio.
Other companies in the same industry, closest in size
Grindr's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.
