Principal Python Engineer - ML Infrastructure
About this role
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
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