E2B
Forward Deployed Engineer
San Francisco · Senior
Sponsorship not specifiedDetected 119 days ago
TypeScriptPythonDatabricksAWSGCPAzureCloud PlatformsTerraformCI/CDLinuxDevOpsPlatform EngineeringAgentic AICybersecurityComplianceEmbedded SystemsDNSFirewallHIPAACommunication
About the role
- You'll define how E2B works with enterprise customers technically - the deployment playbooks, the integration patterns, the reference architectures.
- Your job is to make these customers successful.
- That means sitting inside their architecture, understanding their constraints, and doing whatever engineering work it takes to get E2B running in production.
Responsibilities
- You'll lead these deployments end-to-end: assess their infrastructure, design the network topology (VPC peering, private endpoints, egress controls), provision compute with IaC, wire up observability, validate isolation, and get it to production.
- You'll work inside environments you don't own, with security policies you didn't write, alongside platform teams with their own opinions about how things should work.
- Own the technical success of enterprise deals. From initial technical discovery through production go-live. You're accountable for the customer being live and happy, not just for a successful demo.
Requirements
- 5+ years of software engineering experience, with real depth in at least one of: backend systems, infrastructure/platform engineering, or DevOps/SRE.
- You can explain a complex systems issue to a CTO in two sentences and then pair-program the fix with their senior engineer.
- Strong proficiency in Python and TypeScript.
Nice to have
- Familiarity with AI agent frameworks (LangChain, CrewAI, Vercel AI SDK)
- Deep familiarity with enterprise identity stacks (Okta, Azure AD/Entra, Google Workspace) and how they interact with developer tooling
- Experience with BYOC / managed-service deployment models - deploying and operating your product inside a customer's cloud account, dealing with their IAM boundaries, network restrictions, and compliance requirements
- On-prem or air-gapped deployment experience - bare metal provisioning, offline package mirrors, working without internet access during setup
- Previous FDE, solutions engineer, or professional services role at a developer tools / infrastructure company (Palantir, Databricks, Vercel, HashiCorp, etc.)
- Contributions to open-source projects
- What it's like to work at E2B
- We already generate 8-figure revenue and work directly with top-tier AI companies like Perplexity, Hugging Face, and other exciting teams pushing the frontier of AI.
Benefits
- Go is a bonus.
Company info
- E2B is a fast-growing Series A startup with 8-figure revenue.
- We've raised over $37M since our founding in 2023.
- Our customers include companies like Microsoft, Perplexity, Hugging Face, Manus, and Groq.
- We're building the next hyperscaler for AI agents.
- You'll be E2B's technical point of contact for our most important customers - the ones building AI agents that need secure and scalable sandboxes for their AI agents.
- Many enterprise customers won't use E2B's managed cloud - they need E2B running inside their own AWS/GCP/Azure accounts or on bare-metal infrastructure they control.
- Build custom sandbox templates and environments. Customers have specific runtime requirements - particular system packages, language versions, pre-loaded models, custom filesystems. You'll build and optimize these.
- Feed signal back to product, sales, and engineering. You'll see patterns across customers - what's missing, what's broken, what's confusing. You'll write up proposals, contribute to design docs, and sometimes ship fixes yourself.
- Build repeatable assets. The integration you build for Customer A should become the reference architecture for customers B through Z. You'll write documentation, create example repos, and codify what works.
- Large customers don't just have engineering teams - they have IT, security, and compliance gatekeepers.
- What we're looking for
This listing is sourced directly from E2B's careers page and normalized into a canonical job model.