Systems Software Engineer - Machine Learning Ops

What if your systems engineering skills could directly shape the infrastructure powering the world's most advanced AI models? We're looking for a senior C++ engineer to build and optimize the data pipelines, annotation tooling, and evaluation systems that leading AI labs depend on to train and ship next-generation models. This is a fully remote, flexible contract role for a seasoned engineer who knows C++ deeply and thrives working on high-impact, production-grade systems.

If you've spent years writing performance-critical code and want to apply that expertise at the cutting edge of AI development, this is the role for you.

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

• Design, build, and optimize high-performance C++ systems supporting AI data pipelines and evaluation workflows
• Develop full-stack tooling and backend services for large-scale data annotation, validation, and quality control
• Improve reliability, performance, and safety across existing C++ codebases used in production AI environments
• Collaborate with data, research, and engineering teams to support model training and evaluation workflows
• Identify bottlenecks and edge cases in data and system behavior, and implement scalable, maintainable fixes
• Participate in synchronous design reviews to iterate on system architecture and implementation decisions

• Native or fluent English speaker with clear written and verbal communication skills
• Full-stack developer with a strong systems programming background and deep C++ expertise
• 5+ years of professional experience writing production C++ code
• Experienced working with the C++ frontends of ML frameworks or inference runtimes
• Familiar with hardware acceleration APIs for optimizing model inference
• Able to commit 20–40 hours per week with consistency and reliability

• Prior experience with data annotation, data quality pipelines, or evaluation systems
• Familiarity with AI/ML workflows, model training, or benchmarking pipelines
• Experience with distributed systems design or developer tooling
• Background in performance profiling, debugging, or systems reliability engineering

• Work on real production systems used by leading AI research labs
• Fully remote and flexible — work when and where it suits you
• Freelance autonomy with the structure of meaningful, impactful technical work
• Contribute directly to infrastructure that shapes the future of AI development
• Potential for ongoing work and contract extension as new projects launch