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
US
