Principal Python Engineer - ML Infrastructure

Principal Python Engineer — ML Infrastructure (AI Training) About The Role What if your Python expertise could directly shape the infrastructure powering some of the world's most advanced AI systems? We're looking for a Principal Python Engineer to build and optimize the data pipelines, annotation tooling, and evaluation systems that leading AI labs depend on — working on real production code with meaningful, measurable impact. This is a fully remote, flexible contract role for a senior engineer who thrives at the intersection of systems programming, distributed computing, and AI infrastructure. • 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 large-scale AI data pipelines and model evaluation workflows • Develop full-stack backend tooling and services for data annotation, validation, and quality control at scale • Diagnose and resolve bottlenecks across compute-heavy, distributed systems using advanced async patterns and profiling techniques • Improve reliability, safety, and performance across existing production Python codebases • Collaborate closely with data, research, and engineering teams to accelerate model training and evaluation cycles • Drive architectural decisions through synchronous design reviews and clear technical communication Who You Are • 5+ years writing production Python for large-scale infrastructure or platform engineering • Deep expertise in distributed computing, concurrency, and advanced asynchronous programming patterns • Fluent in Python internals — including GIL limitations, memory profiling, and performance optimization for compute-heavy workloads • Experienced full-stack developer with a strong systems programming background • Clear, confident communicator capable of driving technical strategy and architectural decisions • Native or fluent English speaker • Available 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 • Background in distributed systems architecture or developer tooling • Exposure to working directly with AI research teams or labs Why Join Us • Work on real, high-impact production systems used by leading AI research labs • Fully remote and flexible — work when and where it suits you • Freelance autonomy with the depth and structure of meaningful, long-term technical work • Collaborate with top engineers and researchers at the frontier of AI development • Potential for ongoing work and contract extension as new projects launch