Python Insfrastructure Engineer - Model Evaluation

Python Infrastructure Engineer — Model Evaluation (AI Training) 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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