Principal Python Engineer - ML Infrastructure

About The Role

What if your Python expertise could directly shape the infrastructure powering the next generation of AI? We're looking for a Principal Python Engineer to design and build the data pipelines, annotation tooling, and evaluation systems that leading AI labs depend on to train and improve their most advanced models.

This is a fully remote, flexible contract role for senior engineers who thrive on high-complexity, high-stakes infrastructure challenges. If you've spent years optimizing production Python at scale — and you want your work to matter — this is the opportunity.

• Organization: Alignerr
• Type: Hourly Contract
• Location: Remote
• Commitment: 20–40 hours/week

• Design, build, and optimize high-performance Python systems supporting large-scale AI data pipelines and evaluation workflows
• Develop full-stack tooling and backend services for data annotation, validation, and quality control at scale
• Improve reliability, performance, and safety across existing production Python codebases
• Identify and resolve bottlenecks and edge cases in data and system behavior with scalable, well-engineered fixes
• Collaborate with data, research, and engineering teams to support model training and evaluation workflows
• Participate in synchronous design reviews to drive technical strategy and architectural decisions

• Native or fluent English speaker with clear written and verbal communication skills
• 5+ years of professional experience writing production Python — specifically for large-scale infrastructure or platform engineering
• Deep expertise in designing distributed computing systems and managing concurrency with advanced asynchronous patterns
• Strong command of Python internals — GIL limitations, memory profiling, and performance optimization for compute-heavy workloads
• Full-stack development background with a strong systems programming foundation
• Experienced driving technical strategy and contributing to architectural decisions at a senior or principal level
• Able to commit 20–40 hours per week consistently

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

• Work on real production systems alongside the world's leading AI labs — this is infrastructure that actually ships
• Fully remote and flexible — work on your own schedule from anywhere
• Freelance autonomy with the depth and complexity of principal-level engineering work
• Make a direct, tangible impact on how next-generation AI models are built, evaluated, and improved
• Potential for ongoing work and contract extension as new projects launch