Python Infrastructure Engineer — Model Evaluation (AI Training)
About The Role
What if your Python expertise could directly shape how the world's most advanced AI models are built, tested, and improved? We're looking for a Senior Python Infrastructure Engineer to design and build the data pipelines, annotation tooling, and evaluation systems that leading AI labs depend on to train and validate next-generation models.
This is a fully remote contract role with flexible hours — you'll be working on real production systems at the cutting edge 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 supporting AI data pipelines and model evaluation workflows
- Develop full-stack tooling and backend services for large-scale data annotation, validation, and quality control
- Build and maintain evaluation harnesses for ML models, integrating with inference frameworks
- Improve reliability, performance, and safety across existing Python codebases
- Implement observability, metrics collection, and monitoring to track system reliability and model performance
- Identify bottlenecks and edge cases in data and system behavior, and ship scalable fixes
- Collaborate with data, research, and engineering teams to support model training and evaluation workflows
- Participate in synchronous design reviews to iterate on system 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
- 3–5+ years of professional experience writing production-grade Python
- Experienced building evaluation harnesses for ML models and integrating with inference frameworks
- Strong background in observability, metrics collection, and system reliability monitoring
- Able to commit 20–40 hours per week consistently
- Self-directed and comfortable working asynchronously across distributed teams
Nice to Have
- Prior experience with data annotation, data quality, or evaluation systems
- Familiarity with AI/ML workflows, model training, or benchmarking pipelines
- Experience with distributed systems or developer tooling
- Background in MLOps, infrastructure engineering, or platform engineering
Why Join Us
- Work on real production systems powering some of the most advanced AI research in the world
- Fully remote and flexible — structure your work around your life
- Freelance autonomy with the depth and meaning of high-impact engineering work
- Contribute directly to AI infrastructure that shapes how next-generation models are built and evaluated
- Potential for ongoing work and contract extension as new projects launch
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