Remote Senior Rust Engineer for AI Data Pipelines

Alignerr Corp.; New York (NY); Remote; USD 83,000 - 165,000; Part time

Benefits offered by this job

Remote contract

20–40 hours/week

Job summary

Alignerr is seeking a senior Rust engineer to design and build high-performance data pipelines, annotation tooling, and evaluation systems for AI workflows. This fully remote contract role focuses on production systems used by leading AI labs, with 20–40 hours per week and flexible scheduling.

You will collaborate with research and engineering teams to ensure reliability, performance, and safety of Rust-based pipelines, while advancing model training and evaluation capabilities.

Qualifications

• 3–5+ years of professional experience writing production Rust.
• Deep command of Rust lifetimes, ownership, and error handling.
• Experience building I/O-bound data pipelines with robust retry logic.
• Ability to work 20–40 hours per week remotely.

Responsibilities

• Design, build, and optimize high-performance Rust systems supporting AI data pipelines and evaluation workflows
• Develop full-stack tooling and backend services for large-scale data annotation, validation, and quality control
• Improve reliability, performance, and safety across existing production Rust codebases
• Identify bottlenecks and edge cases in data and system behavior — then implement scalable, robust fixes
• Collaborate with data, research, and engineering teams to support model training and evaluation workflows
• Participate in synchronous design and code reviews to iterate on architecture and implementation decisions

Skills

Rust

Data pipelines

I/O optimization

Retry logic

Remote collaboration

Tools

Git

Linux

Job description

Alignerr is seeking a senior Rust engineer to design and build high-performance data pipelines, annotation tooling, and evaluation systems for AI workflows. This fully remote contract role focuses on production systems used by leading AI labs, with 20–40 hours per week and flexible scheduling.

You will collaborate with research and engineering teams to ensure reliability, performance, and safety of Rust-based pipelines, while advancing model training and evaluation capabilities.