Systems Software Engineer - Machine Learning Ops

About the role

Systems Software Engineer — Machine Learning Ops (AI Infrastructure)

About The Role

What if your expertise in systems programming could directly shape the infrastructure powering the next generation of AI? We're looking for seasoned C++ engineers to build high-performance data pipelines, annotation tooling, and evaluation systems used by leading AI research labs.

This isn't theoretical work. You'll be writing production code that sits at the heart of real model training and evaluation workflows — alongside engineers and researchers pushing the boundaries of what AI can do.

Organization: Alignerr

Type: Hourly Contract

Location: Remote

Commitment: 20–40 hours/week

What You'll Do

Design, build, and optimize high-performance C++ systems 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 correctness across existing C++ codebases used in production ML environments

Collaborate with research, data, and engineering teams to support model training and benchmarking workflows

Identify bottlenecks, edge cases, and failure modes in data and system behavior — and implement scalable, production-ready fixes

Participate in synchronous design reviews to iterate on 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 foundation

5+ years of professional experience writing production-grade C++

Hands-on experience with C++ frontends of ML frameworks or inference runtimes

Familiar with hardware acceleration APIs for optimizing model inference performance

Able to commit 20–40 hours per week with consistent availability

Nice to Have

Prior experience with data annotation pipelines, data quality systems, or evaluation infrastructure

Familiarity with AI/ML workflows, model training loops, or benchmarking frameworks

Experience building distributed systems or developer-facing tooling

Background working alongside ML researchers or in a fast-moving research engineering environment

Why Join Us

Work directly on cutting-edge AI systems alongside top research labs — this is frontier-level infrastructure work

Fully remote and async-friendly — work from wherever you do your best thinking

Freelance flexibility with the depth and impact of a high-stakes engineering role

See your work translate directly into improvements in how next-generation AI models are built and evaluated

Potential for extended engagement and expanded scope as projects evolve