Principal Python Engineer - ML Infrastructure

About the Role

What if your deep Python expertise could directly shape the infrastructure behind 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, high‑impact contract role for a seasoned engineer who thrives at the intersection of systems programming, distributed computing, and AI infrastructure.

Contract Details

Organization: Alignerr
Type: Hourly Contract
Location: Remote
Commitment: 20–40 hours/week

Responsibilities

• 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.
• Improve reliability, performance, and safety across production Python codebases used by top AI research teams.
• Collaborate with data, research, and engineering teams to accelerate model training and evaluation workflows.
• Identify bottlenecks and edge cases in data and system behavior — then implement scalable, lasting fixes.
• Drive architectural decisions through synchronous design reviews with senior technical stakeholders.

Requirements

• Native or fluent English speaker with strong written and verbal communication skills.
• Full‑stack developer with a deep systems programming foundation and 5+ years writing production Python for large‑scale infrastructure or platform engineering.
• Expert in distributed computing systems and advanced asynchronous concurrency patterns.
• Deep understanding of Python internals — GIL limitations, memory profiling, and compute‑heavy performance optimization.
• Comfortable driving technical strategy and architectural decisions independently.
• Available to commit 20–40 hours per week on a consistent basis.

Nice to Have

• Prior experience with data annotation, data quality systems, or model evaluation pipelines.
• Familiarity with AI/ML workflows, model training, or benchmarking infrastructure.
• Background in distributed systems design or developer tooling.

Benefits

• Work directly with leading AI research labs on real production systems at the frontier of AI development.
• Fully remote and flexible — work from anywhere on a schedule that fits your life.
• Freelance autonomy with the structure and focus of meaningful, high‑stakes engineering work.
• Make a direct, tangible impact on the infrastructure powering the next generation of AI.
• Potential for ongoing engagement and expanded scope as projects evolve.