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 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