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Alignerr

Systems Software Engineer - Machine Learning Ops

Alignerr, San Francisco, California, United States, 94199

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Systems Software Engineer - Machine Learning Ops We look for senior-level full‑stack C++ engineers to design, build, and optimize high‑performance systems that support AI data pipelines and evaluation workflows. The role involves developing backend tooling for large‑scale data annotation, validation, and quality control, improving reliability and performance across existing C++ codebases, and collaborating with data, research, and engineering teams to support model training and evaluation workflows.

Base pay range $50.00/hr – $75.00/hr

About The Job Alignerr connects top technical experts with leading AI labs to build, evaluate, and improve next‑generation models. We work on real production systems and high‑impact research workflows across data, tooling, and infrastructure.

Position Type:

Contract, Remote Commitment:

20–40 hours/week Compensation:

Competitive, hourly (based on experience)

Role Responsibilities

Design, build, and optimize high‑performance systems in C++ 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.

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 fixes.

Participate in synchronous reviews to iterate on system design and implementation decisions.

Qualifications Must‑Have

Native or fluent English speaker.

Full‑stack developer experience with a strong systems programming background.

5+ years of professional experience writing production C++.

Experience working with the C++ front‑ends of ML frameworks or inference runtimes.

Familiarity with hardware acceleration APIs for optimizing model inference.

Clear written and verbal communication skills.

Ability to commit 20–40 hours per week.

Preferred

Prior experience with data annotation, data quality, or evaluation systems.

Familiarity with AI/ML workflows, model training, or benchmarking pipelines.

Experience with distributed systems or developer tooling.

Application Process

Submit your resume.

Complete a short technical screening.

Project matching and onboarding.

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