Systems Software Engineer - Machine Learning Ops

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

Work arrangement
Yes