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Member of Technical Staff (AI Infrastructure Engineer)

San Francisco, CA, US • Posted 1 hour ago • Updated 1 hour ago

Full Time

On-site

USD $220,000.00 - 405,000.00 per year

Fitment

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Job Details

Skills

Amazon Web Services
Research
Artificial Intelligence
Large Language Models (LLMs)
Collaboration
Resource Allocation
Systems Architecture
YAML
Configuration Management
Job Scheduling
Python
C++
PyTorch
Storage
Real-time
Debugging
Scheduling
GPU
Computer Cluster Management
CUDA
TensorFlow
Parallel Computing
Computer Networking
Terraform
Ansible
Optimization
Kubernetes
HPC
Management
DevOps
Training
High Availability
Machine Learning (ML)
System Deployment
FOCUS
Orchestration
Resource Management

Summary

We are looking for an AI Infra engineer to join our growing team. We work with Kubernetes, Slurm, Python, C++, PyTorch, and primarily on AWS. As an AI Infrastructure Engineer, you will be partnering closely with our Inference and Research teams to build, deploy, and optimize our large-scale AI training and inference clusters
Responsibilities

Design, deploy, and maintain scalable Kubernetes clusters for AI model inference and training workloads
Manage and optimize Slurm-based HPC environments for distributed training of large language models
Develop robust APIs and orchestration systems for both training pipelines and inference services
Implement resource scheduling and job management systems across heterogeneous compute environments
Benchmark system performance, diagnose bottlenecks, and implement improvements across both training and inference infrastructure
Build monitoring, alerting, and observability solutions tailored to ML workloads running on Kubernetes and Slurm
Respond swiftly to system outages and collaborate across teams to maintain high uptime for critical training runs and inference services
Optimize cluster utilization and implement autoscaling strategies for dynamic workload demands

Qualifications

Strong expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management
Hands-on experience with Slurm workload management, including job scheduling, resource allocation, and cluster optimization
Experience with deploying and managing distributed training systems at scale
Deep understanding of container orchestration and distributed systems architecture
High level familiarity with LLM architecture and training processes (Multi-Head Attention, Multi/Grouped-Query, distributed training strategies)
Experience managing GPU clusters and optimizing compute resource utilization

Required Skills

Expert-level Kubernetes administration and YAML configuration management
Proficiency with Slurm job scheduling, resource management, and cluster configuration
Python and C++ programming with focus on systems and infrastructure automation
Hands-on experience with ML frameworks such as PyTorch in distributed training contexts
Strong understanding of networking, storage, and compute resource management for ML workloads
Experience developing APIs and managing distributed systems for both batch and real-time workloads
Solid debugging and monitoring skills with expertise in observability tools for containerized environments

Preferred Skills

Experience with Kubernetes operators and custom controllers for ML workloads
Advanced Slurm administration including multi-cluster federation and advanced scheduling policies
Familiarity with GPU cluster management and CUDA optimization
Experience with other ML frameworks like TensorFlow or distributed training libraries
Background in HPC environments, parallel computing, and high-performance networking
Knowledge of infrastructure as code (Terraform, Ansible) and GitOps practices
Experience with container registries, image optimization, and multi-stage builds for ML workloads

Required Experience

Demonstrated experience managing large-scale Kubernetes deployments in production environments
Proven track record with Slurm cluster administration and HPC workload management
Previous roles in SRE, DevOps, or Platform Engineering with focus on ML infrastructure
Experience supporting both long-running training jobs and high-availability inference services
Ideally, 3-5 years of relevant experience in ML systems deployment with specific focus on cluster orchestration and resource management

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 91140125
Position Id: b011982dd5c556e5c9adbd2dbd9b5806
Posted 1 hour ago

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Member of Technical Staff (AI Infrastructure Engineer)

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