Senior Python Full-Stack Engineer — AI Data & Infrastructure

What if your Python expertise could directly shape the data pipelines and evaluation systems powering the world's most advanced AI models? We're looking for Senior Python Full-Stack Engineers to design and build the critical infrastructure that leading AI labs depend on — from high-performance data annotation tooling to scalable evaluation workflows. This is a fully remote, flexible contract role with serious technical depth and real production impact.

About the role

This is a fully remote, flexible contract role for a Senior Python Full-Stack Engineer. You’ll design and build high-performance Python systems that support AI data pipelines and evaluation workflows, with a commitment of 20–40 hours per week.

Responsibilities

• Design, build, and optimize high-performance Python systems supporting AI data pipelines and evaluation workflows
• Develop full-stack tooling and backend services for large-scale data annotation, validation, and quality control
• Improve reliability, performance, and safety across production Python codebases
• Collaborate with data, research, and engineering teams to support model training and evaluation workflows
• Identify bottlenecks and edge cases in data and system behavior — then implement scalable, lasting fixes
• Participate in synchronous design reviews to iterate on architecture and implementation decisions

Requirements

• Native or fluent English speaker with clear written and verbal communication skills
• Experienced full-stack developer with a strong systems programming background
• 5+ years of professional experience writing production-grade Python
• Deep understanding of performance optimization and concurrency — asyncio, multiprocessing, threading
• Comfortable with type safety tooling such as Pydantic and mypy
• Proven track record building robust backend services (FastAPI, Django) or scalable data pipelines
• Able to commit 20–40 hours per week with reliability and professionalism

Nice to Have

• Prior experience with data annotation, data quality systems, or evaluation pipelines
• Familiarity with AI/ML workflows, model training, or benchmarking infrastructure
• Experience with distributed systems or developer tooling
• Background working directly with research or ML engineering teams

Benefits

• Work on real production systems alongside leading AI research labs
• Fully remote and flexible — structure your hours around your life
• Freelance autonomy with the substance and challenge of high-impact technical work
• Make a direct, measurable contribution to the infrastructure powering next-generation AI
• Potential for ongoing work and contract extension as new projects launch

Schedule

20–40 hours per week, fully remote and flexible.