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