Python Insfrastructure Engineer - Model Evaluation
About this role
About the Role What if your Python expertise could directly shape the systems that power next-generation AI models? We're looking for a senior Python engineer to design and build the data pipelines, evaluation harnesses, and annotation infrastructure that leading AI labs depend on to train and benchmark their models. This is a high-impact, fully remote contract role working on real production systems — not toy projects. You'll collaborate directly with data, research, and engineering teams at the frontier 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 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 Why Join Us - Work on cutting-edge AI projects alongside leading research labs at the frontier of the field - Fully remote and async-friendly — work from wherever you do your best work - Freelance autonomy with the substance of meaningful, high-impact engineering work - Make a direct, tangible contribution to the systems that shape how AI models are built and evaluated - Potential for ongoing work and contract extension as new projects launch
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