Python Insfrastructure Engineer - Model Evaluation

What if your Python expertise could directly shape how the world's most advanced AI models are built, evaluated, and improved? We're looking for a Senior Python Infrastructure Engineer to design and build the data pipelines, evaluation harnesses, and annotation tooling that power next-generation AI systems at leading research labs.

This is a fully remote contract role with serious technical depth - the kind of work that ships to production and influences model quality at scale.

• Organization:
  Alignerr
• Type:
  Hourly Contract
• Location:
  
  Remote
• Commitment: 20-40 hours/week

• 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

• 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

• 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

• Work on real production systems at the frontier of AI development alongside leading research labs
• Fully remote and flexible - work from wherever you do your best work
• Freelance autonomy with the structure of high-impact, technically challenging projects
• Make a direct, measurable contribution to how next-generation AI models are evaluated and improved
• Potential for ongoing work and contract extension as new projects launch