Remote Senior Python Data Platform Engineer for Pipelines

Alignerr Corp.; Seattle (WA); On-site; USD 83,000 - 165,000; Full time

Job summary

Alignerr is seeking a Senior Python Full-Stack Engineer to design and build data pipelines, annotation tooling, and evaluation systems used to train and assess leading AI models. This is a fully remote, hourly contract role requiring 20-40 hours per week.

You will develop high-performance Python systems, implement backend services for data annotation and quality control, and collaborate with research teams to support model training and evaluation workflows.

Qualifications

• 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.

Responsibilities

• 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.

Skills

Python

Full-stack

Data engineering

Systems programming

English communication

Tools

Airflow

Cloud SDKs (Python)

Job description

Alignerr is seeking a Senior Python Full-Stack Engineer to design and build data pipelines, annotation tooling, and evaluation systems used to train and assess leading AI models. This is a fully remote, hourly contract role requiring 20-40 hours per week.

You will develop high-performance Python systems, implement backend services for data annotation and quality control, and collaborate with research teams to support model training and evaluation workflows.