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Python Insfrastructure Engineer - Model Evaluation

$50-75/hrRemoteFreelanceCODING

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 engineer to design and build the data pipelines, evaluation harnesses, and annotation tooling that sit at the heart of cutting-edge AI development.

This is a fully remote, flexible contract role working alongside leading AI research labs on real production systems. If you're a strong Python engineer who wants to do meaningful, high-impact work at the frontier of AI — this is the role for you.

  • 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 ML inference frameworks
  • Improve reliability, performance, and safety across existing Python codebases
  • Instrument systems with observability and metrics collection to monitor reliability and model performance
  • Identify bottlenecks and edge cases in data and system behavior, and implement scalable fixes
  • Collaborate with data, research, and engineering teams to support model training and evaluation workflows
  • Participate in synchronous design reviews to iterate on 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
  • Solid background in observability, metrics collection, and monitoring for production systems
  • Self-motivated and reliable — able to commit 20–40 hours per week

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 or AI infrastructure

Why Join Us

  • Work directly on cutting-edge AI projects alongside leading research labs
  • Fully remote and flexible — structure your work week around your life
  • Freelance autonomy with the depth and consistency of meaningful, long-term technical work
  • Make a tangible impact on how next-generation AI models are evaluated and improved
  • Potential for ongoing work and contract extension as new projects launch