Modular

2 months ago

Backend Engineering Manager, Cloud Inference

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 2 hours agoStatus: Live

Job Summary

Lead and grow a backend engineering team building cloud inference services on Kubernetes, delivering reliable, secure, and high-performance distributed inference infrastructure. Drive execution, incident readiness, and engineering best practices while partnering with product and security teams to raise reliability and operational maturity.

Location: United States, Canada

Workplace: Remote

Employment Type: Full time

Job Function: Software Engineering

Seniority: Manager level

Key Responsibilities

•Lead and grow a team of backend engineers building cloud inference services running on Kubernetes (control plane + data plane services).
•Drive execution across projects with clear scope, milestones, and measurable outcomes.
•Own operational excellence: on-call readiness, incident response, root-cause analysis, and prevention (SLOs, alerts, runbooks).
•Partner with stakeholders (product, infrastructure, security, and adjacent engineering orgs) to align priorities, manage tradeoffs, and communicate progress.
•Establish engineering best practices for design reviews, code quality, testing, safe rollouts, and post-incident learning.

Pay and Benefits

Salary: 261,000 - 305,000 annually

Equity and Bonus:Equity

Perks:Health Insurance401kRemote WorkPaid Leave

Key Requirements

•10+ years of experience building and operating production backend systems, with 3+ years of engineering management experience
•Strong distributed systems fundamentals (reliability, scalability, consistency, failure modes) and comfort working on performance tuning
•Hands-on experience operating services on Kubernetes in a cloud environment (deployments, networking, scaling, service mesh or equivalent patterns)
•Experience running on-call rotations and leading incident response with high standards for follow-through
•Ability to translate ambiguous goals into execution plans, and to communicate clearly with engineers and non-engineering stakeholders

Experience:Distributed systemsCloudKubernetes

Skills:KubernetesDockerService meshCloudLinuxObservabilityScaling

Languages:English

Tech Stack:KubernetesDockerService meshCloudObservabilityScaling

Company Brief

Modular

Provides infrastructure and developer tools to build, deploy, and scale large AI models and foundation-model applications, including model hosting, orchestration, and SDKs to accelerate AI product development.

Industry: AI & Machine Learning

Website

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Modular

2 months ago

Backend Engineering Manager, Cloud Inference

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 2 hours agoStatus: Live

Job Summary

Location: United States, Canada

Workplace: Remote

Employment Type: Full time

Job Function: Software Engineering

Seniority: Manager level

Key Responsibilities

•Lead and grow a team of backend engineers building cloud inference services running on Kubernetes (control plane + data plane services).
•Drive execution across projects with clear scope, milestones, and measurable outcomes.
•Own operational excellence: on-call readiness, incident response, root-cause analysis, and prevention (SLOs, alerts, runbooks).
•Partner with stakeholders (product, infrastructure, security, and adjacent engineering orgs) to align priorities, manage tradeoffs, and communicate progress.
•Establish engineering best practices for design reviews, code quality, testing, safe rollouts, and post-incident learning.

Pay and Benefits

Salary: 261,000 - 305,000 annually

Equity and Bonus:Equity

Perks:Health Insurance401kRemote WorkPaid Leave

Key Requirements

•10+ years of experience building and operating production backend systems, with 3+ years of engineering management experience
•Strong distributed systems fundamentals (reliability, scalability, consistency, failure modes) and comfort working on performance tuning
•Hands-on experience operating services on Kubernetes in a cloud environment (deployments, networking, scaling, service mesh or equivalent patterns)
•Experience running on-call rotations and leading incident response with high standards for follow-through
•Ability to translate ambiguous goals into execution plans, and to communicate clearly with engineers and non-engineering stakeholders

Experience:Distributed systemsCloudKubernetes

Skills:KubernetesDockerService meshCloudLinuxObservabilityScaling

Languages:English

Tech Stack:KubernetesDockerService meshCloudObservabilityScaling

Company Brief

Modular

Industry: AI & Machine Learning

Website