Senior Staff MLOps Engineer
Confirmed on the employer's own hiring board on Oct 12, 2026. First seen by Alion on Aug 29, 2026.
Join Grindr as a Senior Staff MLOps Engineer, where you will build and own the infrastructure, tooling, and scalable systems that enable high-impact AI. You will architect and maintain platforms for data ingestion, feature computation, model training, automated evaluation, deployment, and ongoing monitoring. This role requires expertise in MLOps, ML platform engineering, and ML infrastructure, with a strong understanding of cloud platforms and experience building production ML pipelines.
Missions
- Concevoir et maintenir des plateformes pour l'ingestion de données, le calcul des caractéristiques, l'entraînement des modèles, l'évaluation automatisée, le déploiement et la surveillance continue.
- Construire et maintenir des pipelines ML de bout en bout pour l'ingestion de données, le calcul des caractéristiques, l'entraînement, la validation, le déploiement et l'inférence.
- Collaborer avec les ingénieurs ML et les ingénieurs de données pour rationaliser les flux de travail, améliorer la vitesse d'itération des modèles et appliquer les meilleures pratiques MLOps.
Profil recherché
- Excellent engineering fundamentals: Python, SQL, bash, Git- Strong understanding of cloud platforms (AWS, GCP, or Azure)
- Strong experience building production ML pipelines and supporting end-to-end ML workflows
- Bachelor’s degree in CS, Engineering, Mathematics, or related field
- Experience with big data and distributed compute: Snowflake, Spark/pySpark, Airflow, Kubernetes, Docker, Helm
- 5+ years experience in MLOps, ML platform engineering, ML infrastructure, or similar roles
- Ability to produce well-engineered, maintainable software with tests, documentation, and operational rigor
- Experience with data quality frameworks, observability tooling, or experiment tracking systems
- Experience with ML frameworks (PyTorch, TensorFlow) sufficient to support training pipelines and deployment workflows
- Experience implementing full model lifecycle management (from data → training → deployment → monitoring)
- Experience with vector databases, embeddings pipelines, or retrieval systems
- Familiarity with NLP/LLM-based data pipelines or image/vision data 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