Staff MLOps Engineer, Grindr

• Bachelor’s degree in CS, Engineering, Mathematics, or related field
• 5+ years experience in MLOps, ML platform engineering, ML infrastructure, or similar roles
• Strong experience building production ML pipelines and supporting end-to-end ML workflows
• Excellent engineering fundamentals: Python, SQL, bash, Git
• Experience with big data and distributed compute: Snowflake, Spark/pySpark, Airflow, Kubernetes, Docker, Helm
• Experience with ML frameworks (PyTorch, TensorFlow) sufficient to support training pipelines and deployment workflows
• Strong understanding of cloud platforms (AWS, GCP, or Azure)
• Ability to produce well-engineered, maintainable software with tests, documentation, and operational rigor
• Experience with data quality frameworks, observability tooling, or experiment tracking systems

Desirable

• 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
• We’re looking for an exceptional MLOps engineer who’s passionate about enabling ML at scale, (>6M daily active users and 100’s of millions of daily user interactions)
• Someone who loves building robust, automated pipelines; creating reliable production training and inference systems; and establishing the infrastructure and processes that accelerate ML product development across the organization
• In this role, you will shape and execute the strategy for Grindr’s ML platform and end-to-end model lifecycle
• 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