Senior MLOps Architect: Scale ML Platform & Pipelines

Grindr; United States; On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Premium health benefits

401k Matching

Flexible PTO

Equity for all employees

Lifestyle stipend

Fully catered onsite meals

Gender Affiliation Fund

LGBTQ health benefits

Commuter stipend

Community giving stipend

Job summary

Grindr seeks a Staff MLOps Engineer to build and own the infrastructure, tooling, and scalable systems enabling high-impact AI. You’ll architect platforms powering data ingestion, feature computation, model training, automated evaluation, deployment, and monitoring for ML teams across recommendations, LLM-based experiences, ads, and more.

You will design foundational systems to let ML engineers experiment faster, ship models reliably, and operate them with confidence in production.

Qualifications

• Experience building production ML pipelines and platforms.
• Strong ML lifecycle knowledge from data to deployment to monitoring.
• Proven ability to operate ML systems at scale with reliability.

Responsibilities

• Build and own infrastructure, tooling, and scalable AI systems.
• Architect platforms for data ingestion, feature compute, model training, evaluation, deployment and monitoring.
• Shape Grindr's ML platform strategy and end-to-end model lifecycle.
• Stand up feature stores and ensure feature reuse across teams.
• Develop automated ML deployment workflows with CI/CD and reproducibility.
• Implement data quality, drift checks, and observability for ML systems.
• Collaborate with ML engineers and data engineers to speed iteration and enforce best practices.

Skills

ML platform engineering

MLOps

End-to-end ML pipelines

Experiment tracking

CI/CD for ML

Education

Bachelor's degree in CS, Engineering, Mathematics, or related field

Tools

Snowflake

Spark/pySpark

Airflow

Kubernetes

Docker

Helm

PyTorch

TensorFlow

Vector databases

Job description

Grindr seeks a Staff MLOps Engineer to build and own the infrastructure, tooling, and scalable systems enabling high-impact AI. You’ll architect platforms powering data ingestion, feature computation, model training, automated evaluation, deployment, and monitoring for ML teams across recommendations, LLM-based experiences, ads, and more.

You will design foundational systems to let ML engineers experiment faster, ship models reliably, and operate them with confidence in production.