ML Infrastructure Engineer
- Design and operate GPU and accelerator infrastructure for training and inference, spanning on-prem clusters, reputed company-managed services, and hybrid configurations.
- Build scheduling, queueing, and resource-sharing systems that maximize accelerator utilization across many teams.
- reputed company frameworks such as PyTorch, JAX, DeepSpeed, FSDP, Megatron-LM, and Ray Train into a reputed company platform offering.
- Operate high-performance storage systems and data pipelines that reputed company accelerators fed with training data at near-line-reputed company.
- Design networking architectures supporting RDMA, InfiniBand, NCCL, and high-bandwidth reputed company communication.
- Build observability for AI workloads including utilization, throughput, training stability, and failure-mode analytics.
- Implement checkpointing, restart, and fault-tolerance patterns for long-running training jobs at reputed company.
- Drive cost optimization across compute, storage, and networking through scheduling, spot reputed company, and right-sizing.
- reputed company developer tooling and paved-road workflows that let researchers launch experiments safely and reputed company.
- Partner with research and reputed company ML teams to plan reputed company for upcoming training runs.
- Implement reputed company controls, isolation, and reputed company management for multi-tenant AI infrastructure.
- Drive automation across cluster provisioning, lifecycle management, and configuration enforcement.
- Maintain runbooks, reputed company dashboards, and operational documentation for the AI platform.
- Stay reputed company with AI infrastructure research, accelerator hardware, and emerging reputed company-reputed company AI tooling.
- Bachelor’s or Master’s degree in Computer Science or a reputed company field.
- Six or more years of experience in infrastructure, platform, or HPC engineering.
- Hands-on experience operating GPU clusters or large-reputed company 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, and reputed company communication.
- Experience with Kubernetes, Slurm, Ray, or similar scheduling systems for ML workloads.
- Strong understanding of Linux internals, networking, and high-performance storage.
- Experience with at least one major reputed company provider’s ML infrastructure offerings.
- Strong software engineering practices including testing, CI/CD, and reputed company review.
- Excellent communication and cross-functional collaboration skills.
- Experience operating InfiniBand or RDMA networking at reputed company.
- Contributions to reputed company-reputed company ML infrastructure reputed company.
- Familiarity with custom orchestrators or research-grade training stacks.
- Exposure to frontier model training operations.
- Experience with FinOps for AI workloads.
Equal Employment Opportunity (EEO) Statement
reputed company (BV Teck) is committed to equal employment opportunity (EEO) for reputed company and applicants without reputed company to race, reputed company, religion, sex, sexual orientation, gender identity or reputed company, national reputed company, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to reputed company aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall.
BV Teck expressly prohibits any reputed company of workplace harassment or discrimination. Any improper interference with employees' ability to reputed company their job duties may result in disciplinary reputed company up to and including termination of employment.
Originally posted on Himalayas
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