Principal Python Engineer - ML Infrastructure

About the role

What if your deep Python expertise could directly shape the infrastructure powering 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, flexible contract role for engineers who thrive at the intersection of systems-level thinking and real production impact. If you have serious Python chops and want to work on infrastructure that actually matters, this is it.

Organization: Alignerr • Type: Hourly Contract • Location: Remote

Responsibilities

• Design, build, and optimize high-performance Python systems supporting large-scale AI data pipelines and evaluation workflows
• Develop full-stack tooling and backend services for data annotation, validation, and quality control at scale
• Improve reliability, performance, and safety across existing Python codebases
• Identify bottlenecks and edge cases in data and system behavior — then implement scalable, production-grade fixes
• Collaborate closely with data, research, and engineering teams to support model training and evaluation pipelines
• Participate in synchronous design reviews to drive architectural decisions and iterate on implementation

Requirements

• Native or fluent English speaker with strong written and verbal communication skills
• 5+ years of professional experience writing production Python for large-scale infrastructure or platform engineering
• Deep expertise in distributed computing systems and advanced asynchronous concurrency patterns
• Solid understanding of Python internals — GIL limitations, memory profiling, and performance optimization for compute-heavy workloads
• Full-stack development experience with a strong systems programming background
• Able to drive technical strategy and communicate architectural decisions clearly across teams

Nice to Have

• Prior experience with data annotation, data quality pipelines, or AI evaluation systems
• Familiarity with ML/AI workflows, model training, or benchmarking infrastructure
• Experience building distributed systems or developer tooling
• Background working directly with AI or research engineering teams

Benefits

• Work on real production systems alongside leading AI research labs
• Fully remote and flexible — work on your own schedule from anywhere
• Freelance autonomy with the substance of high-impact, technically demanding work
• Build infrastructure that directly influences how next-generation AI models are trained and evaluated
• Potential for ongoing work and contract extension as new projects launch

Schedule

Commitment: 20–40 hours per week (flexible)