Senior Python Full-Stack Engineer — AI Data & Infrastructure

About The Role

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. If you've spent years writing Python that actually ships and scales, this is where that experience pays off.

• 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 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

• 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

• 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

• 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