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 benchmark AI model performance
• Improve reliability, performance, and safety across existing Python codebases
• Instrument systems with observability tooling — metrics, logging, and monitoring to track system reliability and model performance
• Identify bottlenecks and edge cases in data and system behavior, and implement scalable fixes
• Collaborate in synchronous design reviews to iterate on architecture and implementation decisions

Who You Are

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

Nice to Have

• Prior experience with data annotation platforms, data quality systems, or evaluation pipelines
• Familiarity with AI/ML workflows, model training, or benchmarking infrastructure
• Experience with distributed systems or developer tooling at scale
• Background in MLOps, data engineering, or research engineering environments