Senior Python Full-Stack Engineer — AI Data & Infrastructure

About The Role

What if your Python expertise could directly shape the infrastructure behind the world's most advanced AI systems? We're looking for a Senior Python Full-Stack Engineer to build and optimize the data pipelines, annotation tooling, and evaluation systems that leading AI labs depend on to train and improve next-generation models. This is a fully remote contract role with meaningful, high-impact work — not filler tasks. You'll be working on real production systems alongside data, research, and engineering teams at the frontier 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
• Improve reliability, performance, and safety across existing 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, and implement scalable fixes
• 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
• 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 consistently

Nice to Have

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