2 дня назад
AI Systems Engineer
90 000 - 100 000$
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
AI Systems Engineer (GPU Infrastructure): Design, build, and operate the platform layer powering large-scale AI training and inference workloads with an accent on GPU clusters, distributed training, scheduling, storage performance, and developer experience. Focus on building reliable and cost-efficient AI infrastructure, optimizing interactions between hardware, kernels, schedulers, and ML frameworks, and operating production systems at scale.
Location: 100% remote within the United States
Salary: $90,000–$100,000 annually
Company
is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States.
What you will do
- Design, build, and operate the platform layer for large-scale AI training and inference workloads.
- Operate and optimize GPU clusters, distributed training frameworks, scheduling systems, and high-performance storage.
- Improve reliability, infrastructure efficiency, and cost control for AI workloads.
- Build developer experience capabilities for ML engineers and researchers.
- Apply software engineering practices including testing, CI/CD, and code review.
- Collaborate with ML engineers, researchers, and cross-functional stakeholders.
Requirements
- Bachelor’s or Master’s degree in Computer Science or a related field.
- 6+ years of experience in infrastructure, platform, or HPC engineering.
- Hands-on experience operating GPU clusters or large-scale ML training infrastructure.
- Strong proficiency in Python and at least one systems language such as Go or C++.
- Deep understanding of distributed training, accelerator architectures, collective communication, Linux internals, networking, and high-performance storage.
- Experience with Kubernetes, Slurm, Ray, or similar ML workload scheduling systems, plus a major cloud provider’s ML infrastructure offerings.
Nice to have
- Experience operating InfiniBand or RDMA networking at scale.
- Contributions to open-source ML infrastructure projects.
- Familiarity with custom orchestrators or research-grade training stacks.
- Exposure to frontier model training operations.
- Experience with FinOps for AI workloads.
Culture & Benefits
- Full-time direct W-2 employment.
- Career growth opportunities within an established organization.
- Equal employment opportunity for employees and applicants.
Hiring process
- Submit a resume by email for consideration.
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