Systems Software Engineer - Machine Learning Ops

Systems Software Engineer — Machine Learning Ops
About The Role
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
What You'll Do
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
Who You Are
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
Nice to Have
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
Why Join Us
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

Work arrangement
Yes