Principal Python Engineer — ML Infrastructure (AI Training) About The Role What if your deep Python expertise could directly shape the infrastructure powering the world's most advanced AI systems? 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 next-generation models. This is a fully remote, flexible contract role for engineers who thrive at the intersection of systems-level thinking and real production impact. If you have serious Python chops and want to work on infrastructure that actually matters, this is it. 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 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 Python codebases Identify bottlenecks and edge cases in data and system behavior — then implement scalable, production-grade fixes Collaborate closely with data, research, and engineering teams to support model training and evaluation pipelines Participate in synchronous design reviews to drive architectural decisions and iterate on implementation Who You Are Native or fluent English speaker with strong written and verbal communication skills 5+ years of professional experience writing production Python for large-scale infrastructure or platform engineering Deep expertise in distributed computing systems and advanced asynchronous concurrency patterns Solid understanding of Python internals — GIL limitations, memory profiling, and performance optimization for compute-heavy workloads Full-stack development experience with a strong systems programming background Able to drive technical strategy and communicate architectural decisions clearly across teams Available to commit 20–40 hours per week

Nice to Have

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

Why Join Us

Work on real production systems alongside leading AI research labs Fully remote and flexible — work on your own schedule from anywhere Freelance autonomy with the substance of high-impact, technically demanding work Build infrastructure that directly influences how next-generation AI models are trained and evaluated Potential for ongoing work and contract extension as new projects launch