Senior Systems Engineer — AI Data & Infrastructure

Company: Alignerr

Location: Location not specified (Remote)

Type: Full-time

Remote: Yes

Posted: 2026-06-09

About this role

About The Role
What if your systems engineering expertise could directly shape how the world's most advanced AI models are built, trained, and evaluated? We're looking for Senior Systems Engineers to work alongside leading AI labs — designing and building the high-performance pipelines, annotation tooling, and evaluation infrastructure that make next-generation AI possible.


This is a fully remote contract role for engineers who move fast, think deeply about systems, and want their work to matter. If you write production-grade Go, Rust, Python, or C++ and care about correctness, performance, and scale — this is built for you.

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

What You'll Do

  • Design, build, and optimize high-performance backend systems using Go, Rust, Python, or C++
  • Develop tooling and services powering large-scale data annotation, validation, and evaluation workflows
  • Build and maintain reliable data pipelines that support AI model training and benchmarking
  • Improve performance, scalability, and correctness across production codebases
  • Collaborate with data, research, and engineering teams on model-facing infrastructure
  • Identify bottlenecks, edge cases, and failure modes across systems and data flows
  • Participate in architecture and implementation reviews to continuously raise the bar

Who You Are

  • 5+ years of professional experience in Go, Rust, Python, or C++
  • Strong systems or backend engineering background with production experience
  • Proven track record building reliable services, internal tooling, or data pipelines
  • Sharp instincts for performance, reliability, and correctness at scale
  • Clear communicator — both in writing and in technical discussions
  • Able to commit 20–40 hours per week consistently

Nice to Have

  • Experience with data annotation, data quality systems, or evaluation pipelines
  • Familiarity with AI/ML workflows, model training, or ben...

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