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Grindr

LGBTQ-focused location-based social dating app

Senior Staff MLOps Engineer

Applied Machine LearningConfirmed live in the last 24 hours
Full-time$190k - $315k /yr

About the Job

Requirements

  • A bachelor's degree in Computer Science, Engineering, Mathematics, or a related field is required.
  • At least 5 years of experience in MLOps, ML platform engineering, ML infrastructure, or similar roles is required.
  • Strong experience building production machine learning pipelines and supporting end-to-end machine learning workflows is required.
  • Strong engineering fundamentals in Python, SQL, Bash, and Git are required.
  • Experience with big data and distributed computing tools, including Snowflake, Spark or PySpark, Airflow, Kubernetes, Docker, and Helm, is required.
  • Experience with machine learning frameworks such as PyTorch and TensorFlow sufficient to support training pipelines and deployment workflows is required.
  • A strong understanding of cloud platforms such as AWS, GCP, or Azure is required.
  • The ability to produce well-engineered, maintainable software with tests, documentation, and operational rigor is required.
  • Experience with data quality frameworks, observability tooling, or experiment tracking systems is required.

Responsibilities

  • Build and maintain end-to-end machine learning pipelines for data ingestion, feature computation, model training, validation, deployment, and inference at substantial data scale.
  • Stand up and manage a feature store, ensuring feature consistency, lineage, and reuse across teams.
  • Apply tools for managing deployment, scheduling, and environments to the specialized context of machine learning infrastructure.
  • Develop automated model deployment workflows with CI/CD, safe rollout strategies, and reproducibility guarantees.
  • Implement monitoring and observability for machine learning 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 machine learning engineers and data engineers to streamline workflows, improve model iteration speed, and enforce MLOps best practices.
  • Ensure the reliability, scalability, and maintainability of machine learning systems through strong engineering and operational rigor.

Desired Qualifications

  • Experience implementing full model lifecycle management, from data through training and deployment to monitoring.
  • Experience with vector databases, embeddings pipelines, or retrieval systems.
  • Familiarity with NLP or LLM-based data pipelines, or image and vision data workflows.
  • Experience with recommendation system infrastructure.
  • A strong grasp of classical machine learning concepts as they relate to platform design.
  • Knowledge of data governance, compliance, retention, and classification.
  • A track record of partnering with research or machine learning teams to operationalize models at scale.

About the Company

Grindr

LGBTQ-focused location-based social dating app

Grindr is a social networking and dating app designed for gay, bi, trans, and queer people. It uses location-based technology to help users discover and connect with others nearby, across many countries. Users create a profile, browse nearby matches, and communicate through messages; Grindr offers free access with optional paid features. The product supports ads, a premium subscription called Grindr Unlimited, and in-app purchases, alongside resources about sexual health and safety. Grindr stands out by focusing exclusively on the LGBTQ community, creating a space that is tailored to this audience and highly engaging for advertisers seeking this demographic. Its goal is to help LGBTQ individuals connect socially, romantically, and professionally while offering community resources and inclusive content.

Company size
201-500
Company stage
IPO
Headquarters
Los Angeles, CA
Founded
2009

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