Applied Industrial Technologies

5 months ago

Engineering Manager - ML Platform and Infrastructure

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 10 hours agoStatus: Live

Job Summary

Engineering Manager leading infrastructure for ML at scale, overseeing Training & Inference Orchestration, GPU Cluster Architecture, and Performance Optimization. You’ll grow and mentor a high‑performing team, design distributed training/inference frameworks across thousands of GPUs, scale GPU cluster infrastructure, optimize throughput and cost, and align roadmaps with research milestones and production needs.

Location: Sunnyvale

Workplace: Onsite

Employment Type: Full time

Job Function: DevOps, Cloud & Infrastructure

Seniority: Manager level

Key Responsibilities

•Grow and manage a team of world-class infrastructure and systems engineers with the goal of delivering a best-in-class ML platform for Physical AI
•Own the design and evolution of frameworks for orchestrating distributed training and inference jobs across thousands of GPUs
•Drive the buildout and scaling of our GPU cluster infrastructure, making critical decisions on architecture, scheduling, networking, and resource management
•Lead efforts to optimize training and inference performance — including throughput, fault tolerance, GPU utilization, and cost efficiency at scale
•Set team goals and roadmap in alignment with research milestones, model development timelines, and production deployment requirements

Pay and Benefits

Salary: USD 204,000 - 343,000 annually

Perks:Health InsuranceDentalVision401kLearning StipendWellness Stipend

Key Requirements

•3+ years of engineering management experience, ideally leading infrastructure or platform teams
•Deep experience with distributed systems, GPU computing, or large-scale ML infrastructure
•Direct experience building or operating large GPU clusters (1,000+ GPUs)
•Strong understanding of distributed training frameworks (e.g., PyTorch Distributed, Megatron-LM, DeepSpeed, FSDP) and job orchestration at scale
•Familiarity with GPU cluster management, high-performance networking (InfiniBand, RDMA), and resource scheduling (Slurm, Kubernetes)

Experience:Distributed systemsGPU computingLarge-scale ML infrastructureGPU clustersTraining frameworks

Skills:LeadershipMentoringTeam_buildingStrategic thinkingCommunication

Languages:English

Tech Stack:PyTorch DistributedMegatron-LMDeepSpeedFSDPSlurmKubernetesInfiniBandRDMAGPUGPU clustersDistributed training

Company Brief

Applied Industrial Technologies

Applied Industrial Technologies is a distributor of industrial products and services, supplying bearings, power transmission, fluid power, safety products, and engineered solutions to maintenance, repair and operations customers and OEMs.

Industry: Developer Tools

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Cleveland, United States

Founded: 1923

Website LinkedIn

Loading job details...

Preparing the role view and application actions.

Applied Industrial Technologies

5 months ago

Engineering Manager - ML Platform and Infrastructure

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 10 hours agoStatus: Live

Job Summary

Location: Sunnyvale

Workplace: Onsite

Employment Type: Full time

Job Function: DevOps, Cloud & Infrastructure

Seniority: Manager level

Key Responsibilities

•Grow and manage a team of world-class infrastructure and systems engineers with the goal of delivering a best-in-class ML platform for Physical AI
•Own the design and evolution of frameworks for orchestrating distributed training and inference jobs across thousands of GPUs
•Drive the buildout and scaling of our GPU cluster infrastructure, making critical decisions on architecture, scheduling, networking, and resource management
•Lead efforts to optimize training and inference performance — including throughput, fault tolerance, GPU utilization, and cost efficiency at scale
•Set team goals and roadmap in alignment with research milestones, model development timelines, and production deployment requirements

Pay and Benefits

Salary: USD 204,000 - 343,000 annually

Perks:Health InsuranceDentalVision401kLearning StipendWellness Stipend

Key Requirements

•3+ years of engineering management experience, ideally leading infrastructure or platform teams
•Deep experience with distributed systems, GPU computing, or large-scale ML infrastructure
•Direct experience building or operating large GPU clusters (1,000+ GPUs)
•Strong understanding of distributed training frameworks (e.g., PyTorch Distributed, Megatron-LM, DeepSpeed, FSDP) and job orchestration at scale
•Familiarity with GPU cluster management, high-performance networking (InfiniBand, RDMA), and resource scheduling (Slurm, Kubernetes)

Experience:Distributed systemsGPU computingLarge-scale ML infrastructureGPU clustersTraining frameworks

Skills:LeadershipMentoringTeam_buildingStrategic thinkingCommunication

Languages:English

Tech Stack:PyTorch DistributedMegatron-LMDeepSpeedFSDPSlurmKubernetesInfiniBandRDMAGPUGPU clustersDistributed training

Company Brief

Applied Industrial Technologies

Industry: Developer Tools

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Cleveland, United States

Founded: 1923

Website LinkedIn