Senior Staff MLOps Engineer

About the Team:

Grindr is an AI-native platform powering how millions of gay people connect globally. With 15M+ monthly users, 130B+ annual messages, and a team of fewer than 200, we move fast, stay lean, and tackle technical problems at a scale few companies ever see.

We at Grindr believe that AI can revolutionize the dating industry. As a Staff MLOps Engineer, you will build and own the infrastructure, tooling, and scalable systems that make high-impact AI possible. You’ll architect and maintain the platforms that power data ingestion, feature computation, model training, automated evaluation, deployment, and ongoing monitoring for the ML teams building recommendations, LLM-based experiences, ads, visual search, growth, and trust & safety. You will design foundational systems that allow our ML engineers to experiment faster, ship models more reliably, and operate them with confidence in production.

At Grindr, we operate in Grindr Mode. Moderately hardcore day to day, truly hardcore when it counts. It’s about doing great work without burning out. Outcomes over outputs. Smart, driven people who raise your bar, with room to live full lives.

This is a hybrid role based in our Bay Area (SF or Palo Alto) or our Chicago offices and will require you to be in office Tuesdays and Thursdays.

About the Job:

• Build and maintain end-to-end ML pipelines for data ingestion, feature computation, model training, validation, deployment, and inference, all at substantial scale of data

• Stand up and manage a feature store, ensuring feature consistency, lineage, and reuse across teams.

• Expertise with best in class tools for managing deployment, scheduling, and environments and how to use them in the specialized regime of ML Infrastructure.

• Develop automated model deployment workflows with CI/CD, safe rollout strategies, and reproducibility guarantees.

• Implement monitoring and observability for ML systems, including data quality checks, drift detection, performance metrics, and alerting.

• Build and support training environments with experiment tracking, distributed training, hyperparameter tuning, and artifact and environment management.

• Collaborate with ML engineers and data engineers to streamline workflows, improve model iteration speed, and enforce MLOps best practices.

• Ensure reliability, scalability, and maintainability of ML systems through strong engineering and operational rigor.

Role Requirements:

• Bachelor’s degree in CS, Engineering, Mathematics, or related field.

• 5+ years experience in MLOps, ML platform engineering, ML infrastructure, or similar roles.