AI Summary
Design and build high-performance Python systems for large-scale AI data pipelines and evaluation workflows. Collaborate with data, research, and engineering teams to support model training and evaluation pipelines. Develop full-stack tooling and backend services for data annotation, validation, and quality control at scale.
Key Highlights
Design and build high-performance Python systems
Collaborate with data, research, and engineering teams
Develop full-stack tooling and backend services
Key Responsibilities
Design, build, and optimize high-performance Python systems
Develop full-stack tooling and backend services for data annotation, validation, and quality control at scale
Collaborate closely with data, research, and engineering teams
Technical Skills Required
Benefits & Perks
Fully remote and flexible work arrangement
Freelance autonomy with high-impact work
Potential for ongoing work and contract extension
Nice to Have
Prior experience with data annotation, data quality pipelines, or AI evaluation systems
Familiarity with ML/AI workflows, model training, or benchmarking infrastructure
Job Description
Principal Python Engineer — ML Infrastructure (AI Training)
About The Role
What if your deep Python expertise could directly shape the infrastructure powering the world's most advanced AI systems? We're looking for a Principal Python 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 engineers who thrive at the intersection of systems-level thinking and real production impact. If you have serious Python chops and want to work on infrastructure that actually matters, this is it.
- Organization: Alignerr
- Type: Hourly Contract
- Location: Remote
- Commitment: 20–40 hours/week
- Design, build, and optimize high-performance Python systems supporting large-scale AI data pipelines and evaluation workflows
- Develop full-stack tooling and backend services for data annotation, validation, and quality control at scale
- Improve reliability, performance, and safety across existing Python codebases
- Identify bottlenecks and edge cases in data and system behavior — then implement scalable, production-grade fixes
- Collaborate closely with data, research, and engineering teams to support model training and evaluation pipelines
- Participate in synchronous design reviews to drive architectural decisions and iterate on implementation
Interested in remote work opportunities in Development & Programming? Discover Development & Programming Remote Jobs featuring exclusive positions from top companies that offer flexible work arrangements.
- Native or fluent English speaker with strong written and verbal communication skills
- 5+ years of professional experience writing production Python for large-scale infrastructure or platform engineering
- Deep expertise in distributed computing systems and advanced asynchronous concurrency patterns
- Solid understanding of Python internals — GIL limitations, memory profiling, and performance optimization for compute-heavy workloads
- Full-stack development experience with a strong systems programming background
- Able to drive technical strategy and communicate architectural decisions clearly across teams
- Available to commit 20–40 hours per week
Browse our curated collection of remote jobs across all categories and industries, featuring positions from top companies worldwide.
- Prior experience with data annotation, data quality pipelines, or AI evaluation systems
- Familiarity with ML/AI workflows, model training, or benchmarking infrastructure
- Experience building distributed systems or developer tooling
- Background working directly with AI or research engineering teams
- Work on real production systems alongside leading AI research labs
- Fully remote and flexible — work on your own schedule from anywhere
- Freelance autonomy with the substance of high-impact, technically demanding work
- Build infrastructure that directly influences how next-generation AI models are trained and evaluated
- Potential for ongoing work and contract extension as new projects launch
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