Forward Deployed Engineer (Principal)

Broadcom

Confirmed live 2 days ago High trust
Remote

Quick summary

Work type
Remote
Location
CO
Salary
$132,200–$211,500 / yr
Posted
7 days ago
Freshness
Confirmed live 2 days ago
Closes
Feb 15, 2027
Nearby
33 roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $198k
This role $172k
$120k most similar roles pay here $247k

This role pays less than 73% of similar roles. Most pay $170,000–$225,575 — the shaded band above. At the midpoint, this role pays about $172k versus about $198k for comparable roles.

Based on 240 similar postings.

Employer

About Broadcom

Broadcom is a global semiconductor and infrastructure software company that designs and markets a wide range of networking, storage, and wireless connectivity solutions. Industry: Semiconductors & Infrastructure Software

Broadcom currently has 119 open roles on FindRole.

Listed pay typically runs $121,900–$195,000 across 105 roles with salary data.

Most-posted roles

View all roles at Broadcom

At a glance

TL;DR · Forward Deployed Engineer (Principal)

Role Posting Log in to save

The Forward Deployed Engineer (Principal) joins the Infrastructure Software Division as an elite technical authority and field leader. This role functions as a senior extension of R&D, where you will embed with complex enterprise accounts to manage platform modernizations, bare metal re-platforming, and mission-critical AI inferencing deployments. You will build production operators and CRDs, author reference implementations, and influence the multi-year product roadmap. Key responsibilities include architecting vSphere Kubernetes Service and VMware Cloud Foundation topologies while managing large-scale migrations of stateful workloads to cloud-native targets. You will work with technologies including Go, Python, Java, Kubernetes, GitOps, and various data engines like Kafka, Spark, and Flink. The role solves critical infrastructure challenges involving distributed AI models, high-density GPU clusters, and complex stateful systems across multi-cloud environments including OpenShift, EKS, GKE, and AKS.

What does a Forward Deployed Engineer earn?

Median $215000 from 45 postings across 23 companies.

See salary data

Skills

Kubernetes vSphere VMware Cloud Foundation Go Python Java GitOps ArgoCD Flux Cluster API CRDs Kafka Pulsar Milvus Qdrant Redis Ray vLLM Spark Flink OpenShift EKS GKE AKS Rancher NVIDIA vGPU Triton ScyllaDB Cassandra

What you'll do

  • Architect and validate production-grade vSphere Kubernetes Service and VMware Cloud Foundation topologies for mission-critical deployments.
  • Design and deploy production systems for real-time LLM inferencing, RAG pipelines, and distributed AI runtime frameworks.
  • Lead end-to-end technical migration strategies to move legacy bare metal systems and stateful engines to cloud-native Kubernetes targets.
  • Write production operators, CRDs, and prototype integrations in the field to unblock strategic accounts and upstream them into core codebases.
  • Translate complex customer pain points and field workarounds into concrete product requirements for the engineering roadmap.
  • Establish technical patterns for containerizing and orchestrating distributed systems like Kafka, vector databases, and in-memory grids.
  • Define cross-platform integration strategies for clients operating across competing distributions like OpenShift, EKS, GKE, and AKS.
  • Develop best-in-class deployment standards, GitOps frameworks, and CAPI patterns while mentoring the broader engineering team.

What we're looking for

  • Must have 10+ years of software engineering experience with 5+ years in infrastructure, distributed systems, AI platforms, or Kubernetes architectures at scale.
  • Must have 17+ years of related experience if a Bachelor's degree is not held.
  • Mastery of Kubernetes internals, Custom Resource Definitions (CRDs), Cluster API (CAPI), and GitOps architecture.
  • Extensive experience architecting high throughput AI inferencing pipelines and virtualized GPU environments.
  • Proven track record managing distributed stateful workloads including vector databases, in-memory grids, and streaming platforms.
  • Proficiency in developing systems-level infrastructure and automation frameworks using Go, Python, or Java.
  • Experience migrating complex enterprise monoliths and bare metal systems to cloud-native Kubernetes targets.
  • Technical fluency across multiple distributions such as OpenShift, EKS, GKE, AKS, and Rancher.

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