Data Platform Engineer; Python

About The Role

What if your Python expertise could directly shape the infrastructure that trains and evaluates the most advanced AI systems in the world? 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.

This is a fully remote, flexible contract role for an experienced engineer who thrives on high-impact, technically challenging work. If you've spent years building production Python systems and want to apply that experience at the frontier of AI development — this is the role.

• Organization:
  Alignerr
• Type:
  Hourly Contract
• Location:
  
  Remote
• Commitment: 20–40 hours/week

• 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 safety across existing Python codebases
• Collaborate with data, research, and engineering teams to support model training and evaluation workflows
• Identify bottlenecks and edge cases in data and system behavior, and implement scalable, production-ready fixes
• Participate in synchronous design reviews to iterate on system architecture and implementation decisions

• Native or fluent English speaker with clear written and verbal communication skills
• Full-stack developer with a strong systems programming background
• 5+ years of professional experience writing production Python for data engineering
• Proficient with workflow orchestration tools to manage complex dependency graphs
• Experienced with dataframe processing libraries and cloud data warehouse SDKs in Python
• Self-directed and reliable — able to commit 20–40 hours per week and deliver consistently

• 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 or developer tooling
• Background working with or alongside AI research teams

• Work on real production systems used by leading AI research labs
• Fully remote and async-friendly — work from wherever you do your best work
• Freelance autonomy with the structure and consistency of ongoing project-based work
• Make a tangible impact on the infrastructure powering next-generation AI models
• Potential for extended engagement and additional projects as the work evolves