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