Job Description

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

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