Senior Rust Engineer: AI Data Pipelines (Remote)

Alignerr Corp.; United States; Remote; GBP 83,000 - 152,000; Full time

Job summary

Alignerr is seeking a senior Rust engineer to design and build high-performance data pipelines, annotation tooling, and evaluation systems that power AI training and evaluation. This is a fully remote hourly contract, with 20–40 hours per week, embedded with data, research, and engineering teams to solve hard systems problems.

You will own Rust code, optimize performance, ensure reliability, and collaborate across teams to support model training and evaluation workflows.

Qualifications

• 3–5+ years of professional experience writing production-grade Rust.
• Deep command of Rust lifetimes, ownership mechanics, and idiomatic error handling.
• Experience building I/O-bound data pipelines with robust retry/backoff logic for production environments.

Responsibilities

• Design, build, and optimize high-performance systems in Rust 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 Rust codebases.
• Identify and eliminate bottlenecks and edge cases in data and system behavior, implementing scalable, production-ready fixes.
• Collaborate closely 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.

Skills

Rust

English communication

Data pipelines

Production-grade software

Job description

Alignerr is seeking a senior Rust engineer to design and build high-performance data pipelines, annotation tooling, and evaluation systems that power AI training and evaluation. This is a fully remote hourly contract, with 20–40 hours per week, embedded with data, research, and engineering teams to solve hard systems problems.

You will own Rust code, optimize performance, ensure reliability, and collaborate across teams to support model training and evaluation workflows.