Python Insfrastructure Engineer - Model Evaluation

What You'll Do

• Design, build, and optimize high-performance Python systems supporting AI data pipelines and model evaluation workflows
• Develop full-stack tooling and backend services for large-scale data annotation, validation, and quality control
• Build and maintain evaluation harnesses that integrate with inference frameworks and benchmarking pipelines
• Improve reliability, performance, and safety across existing Python codebases
• Instrument systems with observability tooling and metrics collection to monitor model performance and system health
• Identify bottlenecks and edge cases in data and system behavior, and implement scalable, maintainable fixes
• Collaborate with data, research, and engineering teams through synchronous design reviews and async communication

Who You Are

• Native or fluent English speaker with clear written and verbal communication skills
• 3–5+ years of professional experience writing production-grade Python
• Full-stack developer with a strong systems programming background
• Experienced building evaluation harnesses for ML models and integrating with inference frameworks
• Strong grasp of observability, metrics collection, and system reliability practices
• Able to commit 20–40 hours per week with consistent availability

Nice to Have

• Prior experience with data annotation pipelines, data quality systems, or model evaluation infrastructure
• Familiarity with AI/ML workflows, model training, or benchmarking frameworks
• Experience with distributed systems or internal developer tooling
• Background working directly with AI labs or ML research teams