Principal Python Engineer - ML Infrastructure

Principal Python Engineer — ML Infrastructure (AI Training) About The Role What if your deep Python expertise could directly shape the infrastructure behind 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, high-impact contract role for a seasoned engineer who thrives at the intersection of systems programming, distributed computing, and AI infrastructure. If you've spent years building production-grade Python at scale and want your work to matter at the frontier of AI — 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 AI data pipelines and model evaluation workflows • Develop full-stack tooling and backend services for large-scale data annotation, validation, and quality control • Improve reliability, performance, and safety across production Python codebases used by top AI research teams • Collaborate with data, research, and engineering teams to accelerate model training and evaluation workflows • Identify bottlenecks and edge cases in data and system behavior — then implement scalable, lasting fixes • Drive architectural decisions through synchronous design reviews with senior technical stakeholders Who You Are • Native or fluent English speaker with strong written and verbal communication skills • Full-stack developer with a deep systems programming foundation and 5+ years writing production Python for large-scale infrastructure or platform engineering • Expert in distributed computing systems and advanced asynchronous concurrency patterns • Deep understanding of Python internals — GIL limitations, memory profiling, and compute-heavy performance optimization • Comfortable driving technical strategy and architectural decisions independently • Available to commit 20–40 hours per week on a consistent basis Nice to Have • Prior experience with data annotation, data quality systems, or model evaluation pipelines • Familiarity with AI/ML workflows, model training, or benchmarking infrastructure • Background in distributed systems design or developer tooling Why Join Us • Work directly with leading AI research labs on real production systems at the frontier of AI development • Fully remote and flexible — work from anywhere on a schedule that fits your life • Freelance autonomy with the structure and focus of meaningful, high-stakes engineering work • Make a direct, tangible impact on the infrastructure powering the next generation of AI • Potential for ongoing engagement and expanded scope as projects evolve