Full Time

Systems Software Engineer - Machine Learning Ops

Alignerr
Contract
Technology, Information and Internet
New York, NY
???* / year

Job Description

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

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