Job description

About the Team: Grindr is an AI-native platform powering how millions of gay people connect globally. With 15M+ monthly users, 130B+ annual messages, and a team of fewer than 200, we move fast, stay lean, and tackle technical problems at a scale few companies ever see. We at Grindr believe that AI can revolutionize the dating industry. 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. At Grindr, we operate in Grindr Mode. Moderately hardcore day to day, truly hardcore when it counts. It’s about doing great work without burning out. Outcomes over outputs. Smart, driven people who raise your bar, with room to live full lives. This is a hybrid role based in our Bay Area (SF or Palo Alto) or our Chicago offices and will require you to be in office Tuesdays and Thursdays. About the Job: - 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. Role Requirements: - Bachelor’s degree in CS, Engineering, Mathematics, or related field. - 5+ years experience in MLOps, ML platform engineering, ML infrastructure, or similar roles. - 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 pipelines and deployment workflows. - Strong understanding of cloud platforms (AWS, GCP, or Azure). - Ability to produce well-engineered, maintainable software with tests, documentation, and operational rigor. - Experience with data quality frameworks, observability tooling, or experiment tracking systems. You May Thrive in this Role if You: - 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. Benefits and Perks: - Health, Dental & Vision Full premium coverage for you. Partial coverage for dependents. - Family Formation Up to $300,000 in fertility and family-building support, covering IVF, surrogacy, egg freezing, and adoption. - Retirement: 401(k) with 6% match and immediate vesting. - Compensation: Industry-competitive compensation, company bonus, and equity for every employee. - Gender-Affirming Care : Industry-leading gender-affirming offerings with up to 90% cost coverage, access to Included Health, monthly stipends for HRT, and more. - Time Off & Rest Flexible vacation policy. Two company-wide rest weeks per year. - Other Benefits: Monthly stipends for cell phone, internet, wellness, food, and commuting, breakfast/lunch About Grindr: Grindr is building the Global Gayborhood in your Pocket. With more than 15 million monthly active users, Grindr has become a fundamental part of the gay community and is charting a path to make the world more free, equal, and just. As a public company, we're moving into the next chapter: becoming an AI-native organization that's redefining what's possible for our community. We're embedding AI into our product and operations, moving with startup speed while operating at scale. Everything we do is rooted in impact for our users and outcomes for the business. This isn't social work, it’s impact through excellence. Business success is what gives us the power to serve our community and the seat at tables others can't reach. How we work is defined by Grindr Mode, our operating model that's moderately hardcore day-to-day and truly hardcore when it counts. We think like owners, drive real outcomes, create clarity through action, and push each other to go further than we thought possible. We’re headquartered in West Hollywood with offices in the Bay Area, Chicago, and New York. Our work extends beyond the product through Grindr for Equality, our social impact initiative that's partnered with 100+ community organizations globally to advance safety, health, and human rights for LGBTQ+ people worldwide. We're profitable, growing, and building a team of smart, driven people who bring different backgrounds, experiences, and perspectives to the work. Want to build technology at a massive scale that serves millions? Come join us! Grindr is an equal-opportunity employer. To learn more about how we handle the personal data of applicants, visit our Employee and Candidate Privacy Policy grindr.com.

Requirements

Must-haves

This is a hybrid role based in our Bay Area (SF or Palo Alto) or our Chicago offices and will require you to be in office Tuesdays and Thursdays.
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.
Bachelor’s degree in CS, Engineering, Mathematics, or related field.
5+ years experience in MLOps, ML platform engineering, ML infrastructure, or similar roles.
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 pipelines and deployment workflows.
Strong understanding of cloud platforms (AWS, GCP, or Azure).
Ability to produce well-engineered, maintainable software with tests, documentation, and operational rigor.
Experience with data quality frameworks, observability tooling, or experiment tracking systems.
You May Thrive in this Role if You:
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.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlllmci/cdpythonsqlkubernetesdockerawsgcpazurerestgo

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Wellness