Python Software Engineer - AI Workflows
Python Software Engineer — AI Workflows About The Role What if your Python expertise could directly shape the infrastructure powering next-generation AI models? We're looking for a Senior Python Full-Stack Engineer to design and build the data pipelines, annotation tooling, and evaluation systems that leading AI labs depend on to train and improve their models. 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 evaluation workflows Develop full-stack tooling and backend services for large-scale data annotation, validation, and quality control Improve reliability, performance, and robustness across existing Python codebases Collaborate with data, research, and engineering teams to support model training and evaluation Identify bottlenecks and edge cases in data and system behavior, and implement scalable, production-grade fixes Participate in synchronous design reviews and iterate on system architecture decisions Who You Are 3–5+ years of professional experience writing production-grade Python Full-stack developer with a strong systems programming background Writes clean, maintainable code with proper use of linters, formatters, and testing frameworks Experienced integrating AI services and APIs, handling edge cases, and ensuring robust error reporting Clear and effective written and verbal communicator Nice to Have Prior experience with data annotation, data quality pipelines, or evaluation systems Familiarity with AI/ML workflows, model training, or benchmarking infrastructure Experience with distributed systems or developer tooling at scale
Why Join Us
Work directly with leading AI research labs on production systems that matter Fully remote and flexible — work from wherever you do your best thinking Freelance autonomy with the structure and purpose of high-impact, technically challenging work Get hands-on experience at the cutting edge of AI infrastructure before it becomes mainstream Potential for ongoing work and contract extension as projects evolve and new ones launch