Confirmed on the employer's own hiring board on Oct 13, 2026. First seen by Alion on Aug 29, 2026.
Join Grindr, the world's largest social networking app for the LGBTQ+ community, as a Senior Staff ML Engineer. In this role, you will architect and oversee large-scale recommendation systems, leverage the latest LLMs to enhance user connections, and provide technical leadership across teams. You will also evaluate and integrate emerging AI tools and techniques to keep Grindr's tech stack ahead of the curve. Enjoy a range of benefits, including 100% employer-paid medical, dental, and vision premiums, 401k matching, flexible PTO, equity for all employees, and more.
Missions
- Architect and oversee large-scale recommendation systems to serve millions, balancing performance and innovation.
- Leverage the latest LLMs to process vast conversation data and enhance user connections.
- Prototype, iterate, and ship production-ready ML solutions that solve real problems for our users.
Profil recherché
- Experience with any public cloud environment - AWS, GCP or Databricks- A scrappy mindset-you tackle messy challenges head-on and get real results
- Proven ability to deliver at scale, with fluency in Python and popular ML frameworks
- Experience building machine/deep learning models with at least one common framework such as PyTorch, Tensorflow, or Keras
- Experience using data and deployment technologies (Snowflake, Airflow, Kubernetes, Docker, Helm, Spark, PySpark)
- 10+ years of experience building ML systems with a focus on 0-to-1 system development, platform-scale architecture, and creating new capabilities from scratch. Experience with recommendation systems or similar is a plus
- Expertise in building and maintaining LLM workflows for nuanced, human-driven tasks
- A track record of leading the development of recommendation platforms that hit the mark for diverse user needs
- Thrives in a fast-paced, outcome-driven environment with lots of cross-functional collaboration
- A proven history of setting technical direction, driving adoption of ML systems across organizations, and owning products end-to-end from early-stage concept to full-scale rollout

