ML Infrastructure Engineer

ML Infrastructure Engineer

Vollzeit 60000 - 80000 € / Jahr (geschätzt) Kein Homeoffice möglich
Bright Vision Technologies

Auf einen Blick

  • Aufgaben: Entwickle und betreibe die Infrastruktur für KI-Training und -Inference auf großem Maßstab.
  • Unternehmen: Bright Vision Technologies, ein innovatives Unternehmen mit Fokus auf Chancengleichheit.
  • Vorteile: Wettbewerbsfähiges Gehalt, Gesundheitsleistungen und flexible Arbeitsmöglichkeiten.
  • Weitere Informationen: Tolle Karrierechancen in einem unterstützenden und inklusiven Umfeld.
  • Warum dieser Job: Gestalte die Zukunft der KI mit modernster Technologie und einem dynamischen Team.
  • Qualifikationen: Abschluss in Informatik und umfangreiche Erfahrung in der Infrastrukturtechnik.

Das prognostizierte Gehalt liegt zwischen 60000 - 80000 € pro Jahr.

ML Infrastructure Engineer - Remote

Sponsorship: U.

Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply.

We are unable to sponsor new H-1B visa petitions for this position.

Job Summary

We are seeking an AI Infrastructure Engineer to design, build, and operate the platform layer that powers large-scale AI training and inference workloads.

The role focuses on GPU clusters, distributed training frameworks, scheduling, storage performance, and developer experience for ML engineers and researchers, with strong emphasis on reliability, efficiency, and cost control.

Key Responsibilities

  • Design and operate GPU and accelerator infrastructure for training and inference, spanning on-prem clusters, cloud‑managed services, and hybrid configurations.
  • Build scheduling, queueing, and resource‑sharing systems that maximize accelerator utilization across many teams.
  • Integrate frameworks such as Py Torch, JAX, Deep Speed, FSDP, Megatron‑LM, and Ray Train into a unified platform offering.
  • Operate high‑performance storage systems and data pipelines that keep accelerators fed with training data at near‑line‑rate.
  • Design networking architectures supporting RDMA, Infini Band, NCCL, and high‑bandwidth collective 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 scale.
  • Drive cost optimization across compute, storage, and networking through scheduling, spot capacity, and right‑sizing.
  • Develop developer tooling and paved‑road workflows that let researchers launch experiments safely and efficiently.
  • Partner with research and applied ML teams to plan capacity for upcoming training runs.
  • Implement security controls, isolation, and access management for multi‑tenant AI infrastructure.
  • Drive automation across cluster provisioning, lifecycle management, and configuration enforcement.
  • Maintain runbooks, capacity dashboards, and operational documentation for the AI platform.
  • Stay current with AI infrastructure research, accelerator hardware, and emerging open‑source AI tooling.
  • Required Qualifications
  • Bachelor’s or Master’s degree in Computer Science or a related field.
  • Six or more 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, and collective 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 cloud provider’s ML infrastructure offerings.
  • Strong software engineering practices including testing, CI/CD, and code review.
  • Excellent communication and cross‑functional collaboration skills.
  • Preferred Qualifications
  • Experience operating Infini Band 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 Fin Ops for AI workloads.
  • Equal Employment Opportunity (EEO) Statement

Bright Vision Technologies is an Equal Opportunity Employer.

Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status or any other protected status as defined by applicable federal, state or local laws.

This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall.

BV Teck expressly prohibits any form of workplace harassment or discrimination.

Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.

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ML Infrastructure Engineer Arbeitgeber: Bright Vision Technologies

Bright Vision Technologies bietet eine dynamische und unterstützende Arbeitsumgebung für ML Infrastructure Engineers, die remote arbeiten möchten. Mit einem starken Fokus auf Mitarbeiterentwicklung und innovativen Projekten im Bereich KI-Infrastruktur, profitieren unsere Mitarbeiter von flexiblen Arbeitszeiten, einer offenen Unternehmenskultur und der Möglichkeit, an spannenden Technologien zu arbeiten. Wir fördern die Zusammenarbeit und bieten zahlreiche Weiterbildungsmöglichkeiten, um sicherzustellen, dass unser Team stets an der Spitze der Branche bleibt.

Bright Vision Technologies

Kontaktdaten:

Bright Vision Technologies Recruiting-Team

Wir glauben, dass du diese Fähigkeiten brauchst, um ML Infrastructure Engineer mit Bravour zu bestehen

GPU-Cluster-Betrieb
Verteiltes Training
Python
Go
C++
Kubernetes
Slurm