Senior Staff MLOps Engineer

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Grindr is seeking a Senior Staff MLOps Engineer to build and own the infrastructure and tooling for ML pipelines at scale. This role involves architecting systems for data ingestion, feature computation, model training, deployment, and monitoring. The position is hybrid in Chicago or Bay Area offices.

• Build and maintain end-to-end ML pipelines for data ingestion, feature computation, model training, validation, deployment, and inference at substantial scale.
• Stand up and manage a feature store, ensuring feature consistency, lineage, and reuse across teams.
• 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, and alerting.
• Build and support training environments with experiment tracking, distributed training, hyperparameter tuning, and artifact management.
• Collaborate with ML engineers and data engineers to streamline workflows and enforce MLOps best practices.

• Bachelor's degree in CS, Engineering, Mathematics, or related field.
• 5+ years experience in MLOps, ML platform engineering, or ML infrastructure.
• 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 and deployment.
• Strong understanding of cloud platforms (AWS, GCP, or Azure).
• Ability to produce well-engineered, maintainable software with tests and documentation.
• Experience with data quality frameworks, observability tooling, or experiment tracking systems.

• Experience implementing full model lifecycle management (data to monitoring).
• Experience with vector databases, embeddings pipelines, or retrieval systems.
• Familiarity with NLP/LLM-based data pipelines or image/vision workflows.
• Experience with recommendation system infrastructure.
• Strong grasp of classical ML concepts as they relate to platform design.
• Knowledge of data governance, compliance, retention, and classification.
• Track record of partnering with research/ML teams to operationalize models at scale.