Backend Developer - Data Annotation Systems

Alignerr Corp.; Seattle (WA); Remote; USD 96,000 - 165,000; Full time

Job summary

Alignerr is seeking a Senior Python Full-Stack Engineer to design and optimize data pipelines, annotation tooling, and evaluation systems used by leading AI labs. This is a remote, hourly contract role for a seasoned engineer who ships production-ready code and collaborates with researchers and engineers.

You will build high-performance Python services, async task queues, and real-time data access via WebSockets, while improving reliability and performance across large-scale data systems.

Qualifications

• 3–5+ years of production-grade Python experience.
• Strong full-stack development background.
• Experience with asynchronous task queues and real-time data protocols.
• Excellent communication in English.
• Ability to commit 20–40 hours per week.

Responsibilities

• Design, build, and optimize high-performance Python systems for AI data pipelines.
• Develop backend services for data annotation, validation, and QC at scale.
• Maintain asynchronous task queues for long-running jobs.
• Optimize queries and serve data via real-time protocols like WebSockets.
• Collaborate with data scientists and engineers to support training workflows.

Skills

Python

Full-stack development

Asynchronous queues

WebSockets

Database optimization

Tools

Celery

PostgreSQL

Redis

Job description

Backend Developer — Data Annotation Systems (AI Training)

About The Role

What if your Python expertise could directly shape the infrastructure that trains the world's most advanced AI models? We're looking for a Senior Python Full-Stack Engineer to build and optimize the data pipelines, annotation tooling, and evaluation systems that leading AI labs depend on every day.

This is a fully remote, flexible contract role for a seasoned engineer who wants to work on real production systems — not toy projects — alongside top-tier research and engineering teams.

• 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 large-scale AI data pipelines and model evaluation workflows
• Develop full-stack tooling and backend services for data annotation, validation, and quality control at scale
• Build and maintain asynchronous task queues to handle long-running background jobs reliably
• Optimize database queries for high-read/write workloads and serve data via real-time protocols including WebSockets
• Improve reliability, performance, and safety across existing production Python codebases
• Collaborate with data scientists, researchers, and engineers to support model training and evaluation workflows
• Identify bottlenecks and edge cases across data systems and implement scalable, production-grade fixes
• Participate in synchronous design and code reviews to drive sound architectural decisions

Who You Are

• 3–5+ years of professional experience writing production-grade Python
• Strong full-stack developer with a solid systems programming background
• Experienced building asynchronous task queues and background job systems
• Proficient in optimizing database queries for high-throughput applications
• Comfortable serving and consuming real-time data via WebSockets or similar protocols
• Clear, precise written and verbal communicator — you can articulate technical decisions well
• Native or fluent English speaker
• Able to commit 20–40 hours per week consistently

Nice to Have

• Prior experience with data annotation platforms, data quality systems, or model evaluation infrastructure
• Familiarity with AI/ML workflows, model training pipelines, or benchmarking systems
• Experience designing or maintaining distributed systems
• Background in developer tooling or internal platform engineering

Why Join Us

• Work on cutting-edge AI infrastructure alongside leading research labs — this is real, high-stakes work
• Fully remote and async-friendly — work from wherever you do your best thinking
• Freelance autonomy with the structure and consistency of meaningful, ongoing project work
• Make a direct, tangible impact on how next-generation AI models are built, trained, and evaluated
• Potential for ongoing work and contract extension as new projects and partnerships launch