Machine Learning Engineer

• Own, design, build, and maintain ML CI/CD infrastructure for automated testing, validation, and deployment pipelines for ML models and inference services
• Reduce CI execution times through intelligent parallelization, caching, test selection, and efficient use of compute resources
• Develop automated systems to test model outputs, detect quality regressions, and validate changes across models, GPU architectures, and configurations
• Build continuous performance testing for inference latency, throughput, GPU utilization, and cost
• Automate validation of model pricing, billing configurations, API schemas, and deployments before production
• Develop AI-powered automation and agentic coding systems to diagnose CI failures, identify regressions, propose fixes, and streamline engineering workflows
• Build deployment safeguards, verification, rollback mechanisms, and monitoring
• Identify repetitive ML-team tasks and build automation tools to eliminate engineering toil
• Collaborate closely with Applied ML and ML Performance teams
• Build infrastructure, tools, and model access that help generative media teams move from idea to production at scale

Explore Senior Software Engineer, Machine Learning Infrastructure –… at fal. Review requirements and application details.

Senior Software Engineer, Machine Learning Infrastructure – Automation