Python Insfrastructure Engineer - Model Evaluation
Python Infrastructure Engineer — Model Evaluation (AI Training) About The Role What if your Python expertise could directly shape how the world's most advanced AI models are built, tested, and improved? We're looking for a Senior Python Infrastructure Engineer to design and build the data pipelines, annotation tooling, and evaluation systems that leading AI labs depend on to train and validate next-generation models. This is a fully remote contract role with flexible hours — you'll be working on real production systems at the cutting edge of AI development. Organization: Alignerr Type: Hourly Contract Location: Remote Commitment: 20–40 hours/week
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 for ML models, integrating with inference frameworks Improve reliability, performance, and safety across existing Python codebases Implement observability, metrics collection, and monitoring to track system reliability and model performance Identify bottlenecks and edge cases in data and system behavior, and ship scalable fixes Collaborate with data, research, and engineering teams to support model training and evaluation workflows Participate in synchronous design reviews to iterate on system architecture and implementation decisions Who You Are Native or fluent English speaker with clear written and verbal communication skills Full-stack developer with a strong systems programming background 3–5+ years of professional experience writing production-grade Python Experienced building evaluation harnesses for ML models and integrating with inference frameworks Strong background in observability, metrics collection, and system reliability monitoring Able to commit 20–40 hours per week consistently Self-directed and comfortable working asynchronously across distributed teams
Nice to Have
Prior experience with data annotation, data quality, or evaluation systems Familiarity with AI/ML workflows, model training, or benchmarking pipelines Experience with distributed systems or developer tooling Background in MLOps, infrastructure engineering, or platform engineering
Why Join Us
Work on real production systems powering some of the most advanced AI research in the world Fully remote and flexible — structure your work around your life Freelance autonomy with the depth and meaning of high-impact engineering work Contribute directly to AI infrastructure that shapes how next-generation models are built and evaluated Potential for ongoing work and contract extension as new projects launch