Systems Software Engineer - Machine Learning Ops
Alignerr · Remote · Coding / software engineering
Systems Software Engineer - Machine Learning Ops is an open role at Alignerr, available remotely, with pay listed as $50–$75/hr.
Applications open on Alignerr’s own site. How we make money.
At a glance
- Pay
- $50–$75/hrStated by Alignerr
- Location
- Remote
- Work type
- Coding / software engineering
- Field
- Coding
- Listed
- Since Oct 4, last confirmed Oct 23
How the pay compares
Higher than 46% of the 79 distinct hourly rates advertised for Coding roles right now. Hourly USD, top of each advertised range, one count per distinct rate. Same method as the Pay Index.
From Alignerr’s listing
“Systems Software Engineer — Machine Learning Ops (AI Infrastructure) About the Role What if your C++ expertise could directly shape the infrastructure powering the next generation of AI models?”
Show the full listing
Systems Software Engineer — Machine Learning Ops (AI Infrastructure) About the Role What if your C++ expertise could directly shape the infrastructure powering the next generation of AI models? We're looking for seasoned systems engineers to build, optimize, and scale the data pipelines, annotation tooling, and evaluation systems that leading AI labs depend on every day. This is a fully remote, flexible contract role for senior engineers who want to work on real production systems at the cutting edge of AI development — not toy projects, not demos. The work is high-impact, technically demanding, and genuinely matters. 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 large-scale AI data pipelines and evaluation workflows Develop full-stack tooling and backend services for data annotation , validation, and quality control at scale Improve reliability, performance, and safety across existing C++ codebases used in production ML environments Collaborate with research, data, and engineering teams to support model training and evaluation infrastructure Identify bottlenecks and edge cases in system and data behavior, and implement robust, scalable solutions Participate in synchronous design reviews to iterate on architecture and implementation decisions Who You Are 5+ years of professional experience writing production-grade C++ Full-stack developer with a strong systems programming foundation Experienced working with the C++ frontends of ML frameworks or inference runtimes Familiar with hardware acceleration APIs for optimizing model inference Native or fluent English speaker with clear written and verbal communication skills Able to commit 20–40 hours per week with reliability and professionalism Nice to Have Prior experience with data annotation, data quality pipelines, or evaluation systems Familiarity with AI/ML workflows, model training, or
About Alignerr: part of Labelbox. See the full profile →
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Frequently asked questions
How do I apply for the Systems Software Engineer - Machine Learning Ops role at Alignerr?
Applications go through Alignerr's own portal - HumanSourcer links to the listing and never collects applications or handles hiring. Alignerr's access model is "Open / self-serve" (Open roles / applications). This listing was first seen here on October 4, 2026 and was still live at the last check.
Is the Systems Software Engineer - Machine Learning Ops role at Alignerr remote?
Yes - Alignerr lists this role as remote.
What does the Systems Software Engineer - Machine Learning Ops role at Alignerr pay?
Alignerr lists this role's pay as $50–$75/hr, quoted from the listing rather than estimated.
What kind of work is Systems Software Engineer - Machine Learning Ops?
Coding / software engineering. AI training, evaluation, coding, writing and specialist review
Is Alignerr a legitimate AI-training platform?
Labelbox expert network - and that relationship is rated "Confirmed" here because it can be checked against labelbox.com rather than taken on Alignerr's word. Confidence describes how well the ownership is evidenced. It is not a rating of how the platform treats the people who work for it, which no public source covers reliably.
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