Systems Software Engineer - Machine Learning Ops

About the role

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