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 that powers the most advanced AI systems in the world? We're looking for a Principal Python Engineer in Austin to design and build the data pipelines, annotation tooling, and evaluation systems that leading AI labs depend on — real production work with real impact at scale. This is a fully remote, flexible contract role for a seasoned engineer who thrives in high-performance, distributed environments and wants to work on problems that matter. Organization: AlignerrType: Hourly ContractLocation: RemoteCommitment: 20–40 hours/week What You'll Do Design, build, and optimize high-performance Python systems that power AI data pipelines and evaluation workflowsDevelop full-stack tooling and backend services for large-scale data annotation, validation, and quality controlImprove reliability, performance, and safety across production Python codebasesIdentify bottlenecks and edge cases in data and system behavior — then implement scalable, elegant fixesCollaborate with data, research, and engineering teams to support model training and evaluation workflowsDrive architectural and system design decisions through synchronous technical reviews Who You Are Native or fluent English speaker with strong written and verbal communication skillsSenior full-stack developer with a strong systems programming background5+ years of professional experience writing production Python for large-scale infrastructure or platform engineeringDeep expertise in designing distributed computing systems and managing concurrency with advanced asynchronous patternsIntimately familiar with Python internals — GIL limitations, memory profiling, and performance optimization for compute-heavy workloadsAble to drive technical strategy and architectural decisions clearly and confidentlyAvailable to commit 20–40 hours per week Nice to Have Prior experience with data annotation, data quality, or model evaluation systemsFamiliarity with AI/ML workflows, model training pipelines, or benchmarking infrastructureExperience with distributed systems architecture or internal developer tooling Why Join Us Work directly with leading AI research labs on production systems that shape next-generation modelsFully remote and flexible — structure your work around your life, not the other way aroundFreelance autonomy with the substance of high-impact, technically demanding workCollaborate with top engineers and researchers on problems at the frontier of AI infrastructurePotential for ongoing engagement and expanded scope as projects grow