AI and ML Infra Software Engineer, GPU Clusters

Jobtailor; California (MO); On-site; USD 120,000 - 190,000; Full time

Job summary

Jobtailor is seeking a recent graduate with an MS/PhD in Computer Science to join our AI/ML infrastructure team in the US. You will support HPC workloads, optimize performance, and help scale distributed training using PyTorch, NeMo, or JAX.

You will work with researchers, data engineers, and DevOps to implement accelerated computing, storage, and networking solutions across AWS, GCP, and Azure. Strong scripting and cloud familiarity are essential.

Qualifications

• Recent graduate with an MS, PhD or equivalent experience in Computer Science or related field.
• Proven experience in AI/ML and HPC workloads and infrastructure.
• Hands-on experience in using or operating High Performance Computing (HPC) grade infrastructure.
• In-depth knowledge of accelerated computing (e.g., GPU, custom silicon).
• Storage (e.g., Lustre, GPFS, BeeGFS).
• Scheduling & orchestration (e.g., Slurm, Kubernetes, LSF).
• High-speed networking (e.g., Infiniband, RoCE, Amazon EFA).
• Containers technologies (Docker, Enroot).
• Expertise in running and optimizing large-scale distributed training workloads using PyTorch (DDP, FSDP), NeMo, or JAX.
• Deep understanding of AI/ML workflows, encompassing data processing, model training, and inference pipelines.
• Proficiency in programming & scripting languages such as Python, Go, Bash.
• Familiarity with cloud computing platforms (e.g., AWS, GCP, Azure).
• Experience with parallel computing frameworks and paradigms.
• Passion for continual learning and keeping abreast of new technologies and effective approaches in the AI/ML infrastructure field.
• Excellent communication and collaboration skills

Responsibilities

• Collaborate closely with our AI and ML research teams to understand infrastructure needs and obstacles.
• Monitor and optimize the performance of our infrastructure ensuring high availability, scalability, and efficient resource utilization.
• Help define and improve important measures of AI researcher efficiency, ensuring measurable results.
• Collaborate with researchers, data engineers, and DevOps professionals to build a seamless AI/ML infrastructure ecosystem
• Stay on top of the latest advancements in AI/ML technologies, frameworks, and effective strategies, and promote their implementation within the company.

Skills

AI/ML Infrastructure

HPC

Distributed Training

Python

Go

Bash

Docker

Enroot

Kubernetes

Slurm

LSF

Lustre

GPFS

BeeGFS

Infiniband

RoCE

AWS

GCP

Azure

NeMo

PyTorch

JAX

Data processing

Model training

Inference pipelines

Education

MS or PhD in Computer Science or related field

Tools

Lustre

GPFS

BeeGFS

Slurm

Kubernetes

LSF

Docker

Enroot

Job description

• Collaborate closely with our AI and ML research teams to understand their infrastructure needs and obstacles
• Monitor and optimize the performance of our infrastructure ensuring high availability, scalability, and efficient resource utilization
• Help define and improve important measures of AI researcher efficiency, ensuring that our actions are in line with measurable results
• Collaborate with diverse teams, including researchers, data engineers, and DevOps professionals, to build a seamless and coordinated AI/ML infrastructure ecosystem
• Stay on top of the latest advancements in AI/ML technologies, frameworks, and effective strategies, and promote their implementation within the company

Requirements

• Recent graduate with a MS, PhD or equivalent experience in Computer Science or related field
• Proven experience in AI/ML and HPC workloads and infrastructure
• Hands-on experience in using or operating High Performance Computing (HPC) grade infrastructure
• In-depth knowledge of accelerated computing (e.g., GPU, custom silicon)
• Storage (e.g., Lustre, GPFS, BeeGFS)
• Scheduling & orchestration (e.g., Slurm, Kubernetes, LSF)
• High-speed networking (e.g., Infiniband, RoCE, Amazon EFA)
• Containers technologies (Docker, Enroot)
• Expertise in running and optimizing large-scale distributed training workloads using PyTorch (DDP, FSDP), NeMo, or JAX
• Deep understanding of AI/ML workflows, encompassing data processing, model training, and inference pipelines
• Proficiency in programming & scripting languages such as Python, Go, Bash
• Familiarity with cloud computing platforms (e.g., AWS, GCP, Azure)
• Experience with parallel computing frameworks and paradigms.
• Passion for continual learning and keeping abreast of new technologies and effective approaches in the AI/ML infrastructure field.
• Excellent communication and collaboration skills

Core Competencies

Demonstrates expertise in AI/ML infrastructure, including High Performance Computing (HPC) and accelerated computing technologies. Proficient in optimizing large-scale distributed training workloads and collaborating with diverse teams to enhance AI researcher efficiency.

Highest-signal resume keywords

• AI/ML Infrastructure
• High Performance Computing (HPC)
• Distributed Training Workloads Optimization
• Programming Languages (Python, Go, Bash)
• Cloud Computing Platforms (AWS, GCP, Azure)

ATS Optimization Keywords

Hard Skills

• AI/ML Workloads
• Accelerated Computing
• Storage Technologies (Lustre, GPFS, BeeGFS)
• Scheduling & Orchestration (Slurm, Kubernetes, LSF)
• High-Speed Networking (Infiniband, RoCE, Amazon EFA)
• Container Technologies (Docker, Enroot)
• Parallel Computing Frameworks
• Data Processing
• Model Training
• Inference Pipelines

Soft Skills

• Excellent Communication
• Collaboration Skills
• Passion for Learning

Certifications & Qualifications

• MS or PhD in Computer Science or Related Field