The ad says“Experience with big data and distributed compute: Snowflake, Spark/pySpark, Airflow, Kubernetes, Docker, Helm”
The ad says“Strong understanding of cloud platforms (AWS, GCP, or Azure)”
The ad says“Strong understanding of cloud platforms (AWS, GCP, or Azure)”
The ad says“Excellent engineering fundamentals: Python, SQL, bash, Git”
The ad says“Strong grasp of classical ML concepts as they relate to platform design”
The ad says“Knowledge of data governance, compliance, retention, and classification”
The ad says“Knowledge of data governance, compliance, retention, and classification”
The ad says“Knowledge of data governance, compliance, retention, and classification”
The ad says“Experience with big data and distributed compute: Snowflake, Spark/pySpark, Airflow, Kubernetes, Docker, Helm”
The ad says“Experience with vector databases, embeddings pipelines, or retrieval systems”
The ad says“Strong understanding of cloud platforms (AWS, GCP, or Azure)”
The ad says“Excellent engineering fundamentals: Python, SQL, bash, Git”
The ad says“Experience with big data and distributed compute: Snowflake, Spark/pySpark, Airflow, Kubernetes, Docker, Helm”
The ad says“Familiarity with NLP/LLM-based data pipelines or image/vision data workflows”
The ad says“Experience with big data and distributed compute: Snowflake, Spark/pySpark, Airflow, Kubernetes, Docker, Helm”
The ad says“Familiarity with NLP/LLM-based data pipelines or image/vision data workflows”
The ad says“Familiarity with NLP/LLM-based data pipelines or image/vision data workflows”
The ad says“Experience with big data and distributed compute: Snowflake, Spark/pySpark, Airflow, Kubernetes, Docker, Helm”
The ad says“Excellent engineering fundamentals: Python, SQL, bash, Git”
The ad says“Experience with ML frameworks (PyTorch, TensorFlow) sufficient to support training pipelines and deployment workflows”
The ad says“Experience with recommendation system infrastructure”
The ad says“Knowledge of data governance, compliance, retention, and classification”
The ad says“Experience with vector databases, embeddings pipelines, or retrieval systems”
The ad says“Experience with big data and distributed compute: Snowflake, Spark/pySpark, Airflow, Kubernetes, Docker, Helm”
The ad says“Experience with big data and distributed compute: Snowflake, Spark/pySpark, Airflow, Kubernetes, Docker, Helm”
The ad says“Excellent engineering fundamentals: Python, SQL, bash, Git”
The ad says“Experience with ML frameworks (PyTorch, TensorFlow) sufficient to support training pipelines and deployment workflows”
The ad says“Experience with vector databases, embeddings pipelines, or retrieval systems”
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
Benefits
100% employer-paid medical, dental, and vision premiums for employees
401k Matching (up to 6%)
Flexible PTO, sick-time, holidays, and two company wide shut-down weeks + an annual travel and leisure stipend
Equity for all employees
Monthly lifestyle stipend for food and wellness as well as fully catered onsite breakfast, lunch, and snacks
Gender Affirmation Fund for transgender and gender non-conforming employees
Queer-inclusive health benefits including LGBTQ consultative healthcare and expanded HRT shipped directly to employees in eligible states
Monthly commuter stipend to get you to and from the office on in-person collaboration days
Community projects and charitable giving stipend to support LGBTQ+ non-profit organizations alongside Grindr for Equality
