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 Toronto 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: Alignerr • Type: Hourly Contract • Location: Remote • Commitment: 20–40 hours/week What You'll Do • Design, build, and optimize high-performance Python systems that power AI data pipelines and 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 • Identify bottlenecks and edge cases in data and system behavior — then implement scalable, elegant fixes • Collaborate with data, research, and engineering teams to support model training and evaluation workflows • Drive architectural and system design decisions through synchronous technical reviews Who You Are • Native or fluent English speaker with strong written and verbal communication skills • Senior full-stack developer with a strong systems programming background • 5+ years of professional experience writing production Python for large-scale infrastructure or platform engineering • Deep expertise in designing distributed computing systems and managing concurrency with advanced asynchronous patterns • Intimately familiar with Python internals — GIL limitations, memory profiling, and performance optimization for compute-heavy workloads • Able to drive technical strategy and architectural decisions clearly and confidently • Available to commit 20–40 hours per week Nice to Have • Prior experience with data annotation, data quality, or model evaluation systems • Familiarity with AI/ML workflows, model training pipelines, or benchmarking infrastructure • Experience 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 models • Fully remote and flexible — structure your work around your life, not the other way around • Freelance autonomy with the substance of high-impact, technically demanding work • Collaborate with top engineers and researchers on problems at the frontier of AI infrastructure • Potential for ongoing engagement and expanded scope as projects grow

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