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