Backend Developer - Data Annotation Systems

Backend Developer — Data Annotation Systems (AI Training)
About The Role
What if your Python expertise could directly shape the infrastructure behind 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 to train and improve next-generation models.
This is a fully remote, flexible contract role for an experienced engineer who thrives on high-impact systems work and wants to be close to the frontier of AI development.
Organization: Alignerr
Type: Hourly Contract
Location: Remote
Commitment: 20–40 hours/week
What You'll Do
Design, build, and optimize high-performance Python systems that power AI data pipelines and model evaluation workflows
Develop full-stack backend services and tooling for large-scale data annotation, validation, and quality control
Build and maintain asynchronous task queues to handle complex, long-running background jobs at scale
Optimize database queries for high-read/write workloads and serve data via real-time protocols such as WebSockets
Improve reliability, performance, and safety across existing Python codebases
Collaborate closely with data, research, and engineering teams to support model training and evaluation workflows
Identify bottlenecks and edge cases in system and data behavior, then implement scalable, production-ready fixes
Participate in synchronous design reviews to iterate on architecture and implementation decisions
Who You Are
Native or fluent English speaker with clear written and verbal communication skills
Full-stack developer with a strong systems programming background and 3–5+ years of professional Python experience
Proven experience building and shipping production-grade Python applications
Experienced with asynchronous task queues and background job processing
Skilled at optimizing database performance for demanding, high-throughput applications
Comfortable working with real-time data protocols (e.g., WebSockets)
Self-directed and reliable — able to commit 20–40 hours per week and deliver consistently without hand-holding
Nice to Have
Prior experience with data annotation, data quality pipelines, or model evaluation infrastructure
Familiarity with AI/ML workflows, model training, or benchmarking systems
Experience with distributed systems, developer tooling, or data engineering
Why Join Us
Work directly with leading AI labs on production systems that matter
Fully remote and flexible — structure your work around your schedule
Freelance autonomy with the substance of high-impact engineering work
Get hands-on exposure to the cutting edge of AI infrastructure and research workflows
Potential for ongoing work and contract extension as projects scale

Work arrangement
Yes