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