Backend Developer - Data Annotation Systems

Alignerr Corp.; New York (NY); Remote; USD 83,000 - 152,000; Part time

Job summary

Alignerr Corp. is seeking a Senior Python Full-Stack Engineer for a fully remote, hourly contract role to design data pipelines, annotation tooling and evaluation systems powering AI training.

You will build high-performance Python services, develop data annotation tooling, and manage asynchronous queues and real-time data delivery via WebSockets, collaborating with data science and engineering teams to accelerate model training and evaluation.

Qualifications

• 3–5+ years of professional Python experience.
• Experience building production-grade backend systems.
• Experience with asynchronous task queues and real-time data.
• Able to commit 20–40 hours per week remotely.
• Fluent English communication.

Responsibilities

• Design and optimize Python data pipelines.
• Build full-stack backend services for data annotation.
• Manage asynchronous task queues for long jobs.
• Optimize DB queries for high throughput and use WebSockets.
• Improve reliability and performance of Python code.
• Collaborate with data, research, and Eng teams.
• Identify bottlenecks and implement scalable fixes.
• Participate in design reviews.

Skills

Python

Full-stack

Async queues

WebSockets

Performance

English

Job description

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