Senior Software Engineer, Machine Learning Infrastructure (Tinder LLC, West Hollywood, California)
Gotinder · West Hollywood, CA
View and Apply ↗
Opens the original job posting in a new tab.
Job description
- Design, build, and maintain scalable machine learning (ML) infrastructure to support experimentation, training, deployment, and monitoring of ML models processing large-scale datasets with hundreds of billions of data points.
- Develop and maintain robust, scalable infrastructure platforms that support the needs of machine learning engineers across multiple business units.
- Design, build, and maintain data processing and moderation pipelines that handle large data volumes and integrate with trust and safety workflows.
- Design, develop, and maintain application programming interfaces (APIs), including REST, gRPC, and GraphQL, to support internal ML platform services and system integrations.
- Develop and implement model evaluation, validation, and quality assurance processes, including A/B testing frameworks and automated evaluation systems, to ensure model accuracy, reliability, and performance.
- Design, develop, and maintain scalable ML platform systems and data infrastructure using distributed data technologies, including Apache Spark, Kafka, Flink, and Databricks, to support global data processing and analytics needs.
- Analyze ML infrastructure requirements across business units and design technical solutions within defined scalability, performance, and cost constraints.
- Support technical design and implementation of ML lifecycle infrastructure, including model training, serving, monitoring, feature stores, and evaluation systems, with an emphasis on platform engineering and self-service capabilities.
- Participate in hiring activities by conducting technical interviews and providing input on candidate evaluations.
- Develop and maintain technical documentation, including system designs, operational guides, and internal knowledge bases.
- Design and optimize recommendation systems and moderation data pipelines, applying best practices for data versioning, feature management, and model evaluation.
- Implement and optimization of backend and ML services to ensure reproducibility, reliability, and operational stability.
- Design and optimize large-scale data pipelines and database systems to support efficient data access patterns for ML workflows.
- Collaborate with cross-functional teams, including software engineers, data engineers, and ML engineers, to support the development and deployment of ML-enabled product features.
- Design and maintain infrastructure supporting large language model (LLM) workloads.
- Analyze and resolve complex distributed systems issues affecting performance, scalability, reliability, and availability of high-traffic ML applications. processing jobs, and automation tools supporting the ML lifecycle.