Backend Software Engineer - Enterprise

E2B•San Francisco, CA

4d•$175,000 - $350,000•Hybrid

About The Position

E2B is hiring a Backend Software Engineer to develop Go services for its enterprise orchestration platform, which manages deployments in customers’ AWS, Google Cloud, and Azure environments. The platform coordinates operations across remote Kubernetes clusters and provides tools for engineers to understand and manage the fleet. The role extends APIs and distributed workflows from design through production operation, in collaboration with SREs across Enterprise Engineering to make deployments reliable and easy to operate. The engineer will participate in production on-call for the components they own. The position involves building services and workflows for cluster lifecycle management, creating fleet visibility and diagnostic tools, integrating Kubernetes and cloud infrastructure technologies, and designing APIs and data models for deployment state and configuration. The engineer will own the reliability of their services, work with customer-facing teams on complex deployment issues, and automate manual fleet operations. E2B is a fast-growing Series A startup building the Agent Cloud. The company describes a primarily in-person working arrangement, with four days on-site and one day working from home, at offices in San Francisco and Prague.

Requirements

At least 5 years building and operating production backend systems; infrastructure control planes, deployment automation, or asynchronous services are described as ideal experience.
Strong Go skills, including concurrency, memory management, and profiling.
Strong distributed-systems fundamentals, including durable workflows, state machines, consistency, idempotency, and failure recovery.
Experience designing REST or gRPC APIs, including resource models, authentication, error handling, documentation, and backward-compatible changes.
Experience with PostgreSQL or another relational database, including schema design, indexes, transactions, and migrations.
Experience testing and debugging long-running operations across unreliable networks and remote systems.
Hands-on Kubernetes and Helm experience, including working with cluster APIs and releases.
Ability to debug issues across Go services, cloud IAM, networks, and containers.
Ability to write maintainable, well-tested code and review AI-assisted changes for correctness and fit with the existing system.
Ability to take an ambiguous backend project from design through production.

Nice To Haves

Experience building AI agents or applications that use tools, execute code, or run in sandboxes.
Experience building control planes, Kubernetes operators, or infrastructure APIs.
Experience shipping BYOC, private-cloud, or self-hosted software into customer environments.
Experience building multi-cloud, multi-region, or multi-tenant systems.
Experience with Terraform modules, plans, and state; GitOps, Argo CD, or cloud-provider APIs.
Experience with event-driven systems, message queues, or workflow engines.
Experience with infrastructure, developer-tools, or AI-agent platform products.
TypeScript or Python experience alongside Go.
Open-source contributions in backend, Kubernetes, or distributed systems.

Responsibilities

Build and operate Go services for cluster installation, configuration, scaling, upgrades, recovery, and decommissioning.
Develop distributed workflows that handle partial failures, retries, concurrent changes, and state reconciliation across remote clusters.
Build fleet tools to display cluster inventory, health, configuration drift, rollout progress, and audit history, and help engineers diagnose failures.
Integrate Kubernetes APIs, Helm, and infrastructure packages into repeatable deployment workflows across supported cloud configurations.
Design APIs and data models for deployment state, configuration, and access validation.
Build modular, well-tested orchestration software that other engineers can extend confidently.
Own the reliability of services by adding observability and integration tests, validating releases and recovery behavior, and addressing recurring failures through incident follow-up.
Work with FDE, Solutions, and Support on complex deployment issues and software escalations.
Reduce manual fleet operations through automation and self-service tooling.
Participate in production on-call for owned components.