---
title: 'Infrastructure Security Engineer at Modal'
canonical: 'https://feeny.ai/job/infrastructure-security-engineer-modal-new-york-fjvfe2e55ns4'
type: 'job'
last_seen: '2026-10-13'
---

# Infrastructure Security Engineer at Modal

- **Company:** Modal
- **Location:** New York, NY
- **Employment:** full-time
- **Posted:** 2026-04-21
- **Last confirmed live:** 2026-10-13
- **Apply:** https://jobs.ashbyhq.com/modal/8de60620-09bf-4905-910f-a290814a29ee

## Job description

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like [Lovable](https://modal.com/blog/lovable-case-study), [Ramp](https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal), Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M [Series C](https://modal.com/blog/modal-series-c) at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g.,[Seaborn](https://github.com/mwaskom/seaborn),[Luigi](https://github.com/spotify/luigi)), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.

The Role: We’re looking for an Infrastructure Security Engineer to design and secure the core systems that power our platform. This role focuses on building security directly into our infrastructure—from container isolation and orchestration to identity and secrets management in a multi-tenant, cloud-native environment. You’ll work closely with engineering teams to define secure primitives and ensure our platform is resilient, scalable, and trustworthy by design. This is a hands-on, deeply technical role focused on real systems, not compliance or policy.

What You'll Do: Platform & Runtime Security

- Design and improve isolation mechanisms for multi-tenant workloads (containers, sandboxing, execution environments)
- Strengthen boundaries between customers, workloads, and internal systems
- Identify and mitigate risks in distributed, dynamic compute environments

Container & Orchestration Security

- Secure and harden containerized workloads and orchestration systems (e.g., Kubernetes or similar)
- Improve workload isolation, scheduling boundaries, and runtime protections
- Evaluate tradeoffs in multi-tenant execution models

Identity & Access Management

- Design and improve authentication and authorization systems across services
- Implement strong service-to-service identity and least-privilege access patterns
- Improve access controls across infrastructure and internal systems

Secrets & Key Management

- Build and maintain systems for securely managing secrets, tokens, and credentials
- Improve rotation, auditing, and access controls
- Reduce secret sprawl and integrate secure patterns into developer workflows

Cloud & Infrastructure Security

- Secure cloud environments across providers (AWS, GCP, etc.) with a focus on consistency and portability
- Improve network boundaries, service segmentation, and access controls
- Embed security into infrastructure-as-code and deployment systems

Engineering Partnership

- Work closely with product and infrastructure teams to design secure systems from the ground up
- Review architecture and code for security risks and provide actionable guidance
- Identify patterns in risks and drive cross-cutting improvements

Requirements: Core Experience

- Experience securing cloud-native infrastructure and distributed systems in production
- Background in infrastructure, backend, or security engineering
- Experience working in multi-tenant or high-scale environments

Technical Depth

- Strong understanding of containerization and orchestration systems (e.g., Kubernetes or similar)
- Experience designing or securing isolation mechanisms in multi-tenant systems
- Solid understanding of authentication, authorization, and service identity models
- Experience with secrets management and secure handling of credentials
- Strong foundation in networking concepts (segmentation, service communication, access boundaries)

Mindset

- Builder mentality, you design and implement, not just review
- Pragmatic approach to security in fast-moving environments
- Comfortable working deeply with engineers and influencing system design

Preferred Qualifications:

- Experience with sandboxing or runtime isolation technologies (e.g., gVisor, Firecracker, seccomp, or similar)
- Familiarity with kernel-level or low-level isolation primitives
- Experience securing Kubernetes or similar orchestration systems in production
- Background in developer infrastructure, compute platforms, or multi-tenant systems

## About Modal

## Company Overview
- **One-liner**: Modal provides high-performance AI infrastructure—a serverless, globally distributed GPU cloud for inference, training, sandboxes, and agent workloads.
- **Entity Type**: Private (raised over $466M, Series D from top-tier investors)
- **Headquarters**: New York, NY, USA (with offices in Stockholm, Sweden and San Francisco, CA, USA)
- **Founded**: Not publicly available in provided sources
- **Founders**: Erik Bernhardsson and Akshat Bubna

## Core Business
- Primary industry: Cloud infrastructure / AI compute / Developer tools
- Target customers: Developers and engineering teams building AI/ML products (B2B, from startups to enterprises)
- Mission or purpose: “Make it easier to iterate and ship applications for data, AI, and machine learning” and “Make cloud development work like magic.”

## Products & Services
- **[Modal Runtime](https://modal.com/)** – Serverless container platform with custom file system, scheduler, and container image builder. Sub-second cold starts and instant autoscaling from 0 to 1000+ GPUs.
- **[Modal Sandboxes](https://modal.com/)** – Isolated, ephemeral environments for running untrusted code (e.g., coding agents, RL rollouts). Programmatically spin up fresh environments with custom images.
- **[Modal Inference](https://modal.com/)** – Globally distributed inference with sub-10ms overhead latency, support for token streaming, WebRTC, WebSocket. Supports LLMs, audio, image/video generation.
- **[Modal Training](https://modal.com/)** – Fine-tuning and multi-node training on H100s, A100s, B200s with gang scheduling and InfiniBand networking. Single line of code to scale from single-GPU to multi-node clusters.
- **[Modal Batch / Async Inference](https://modal.com/)** – Run evaluations, embeddings, re-ranking, dataset generation at scale, thousands of GPUs fully parallel.
- **[Modal SDK](https://modal.com/)** – Python SDK that lets developers define infrastructure and workloads in code, then ship to the cloud.

## Market Standing
- **Valuation**: $4.65B (as of 2026, per [jobsbyculture.com](https://jobsbyculture.com/blog/working-at-modal-2026))
- **Key Metric**: Total funding raised – over $466M (per [modal.com/company](https://modal.com/company))
- **Notable Investors/Partners**: General Catalyst, Redpoint Ventures, Lux Capital, Amplify Partners, Creandum (per [modal.com/company](https://modal.com/company))
- **Growth Signals**: ~150 employees (2026); global offices in New York, Stockholm, San Francisco; rapid hiring across engineering, GTM, and G&A roles; strong traction in inference, training, and agent infrastructure.

## Competitive Advantages
- **Deep infrastructure stack**: Custom file system, container runtime, scheduler, and image builder built from scratch to optimize AI workloads.
- **Developer experience**: “Stay in Python, ship to the cloud” – composable primitives that specify everything from logic to hardware in one code file.
- **Instant elasticity**: Scale from zero to 1000+ GPUs in seconds, pay only for compute used (no reserved capacity).
- **Global GPU access with low latency**: Sub-10ms overhead for online inference via globally distributed compute.
- **Compliance and security**: SOC 2 and HIPAA compliant, data residency controls, battle-tested isolation for untrusted code.

## Strategic Focus
- **AI-native runtime**: Deepening support for inference, fine-tuning, reinforcement learning, and agent workflows.
- **Scaling for agents**: Developing sandboxes and execution layers purpose‑built for interactive coding agents and long‑running RL rollouts.
- **Expanding global capacity**: Adding GPU availability across more regions, leveraging elastic cloud capacity.
- **Enterprise readiness**: Investing in security, governance, team controls, and data residency to serve larger customers.

## Why Work Here
- **Culture and team**: Founded by engineers who created open‑source tools (Seaborn, Luigi); team includes academic researchers, olympiad medalists, and experienced engineering leaders. Flat, high‑trust environment.
- **Location / flexibility**: Offices in New York, Stockholm, and San Francisco; likely hybrid/remote‑friendly (many roles list multiple locations).
- **Compensation and growth**: Transparent salary culture (as highlighted in 2026 profile); strong growth trajectory backed by $466M in funding and a $4.65B valuation.
- **Perks**: $30/month free compute for personal projects (customer benefit, likely similar for employees); focus on developer experience and “magic.”

## Sources
1. [modal.com](https://modal.com/)
2. [modal.com/company](https://modal.com/company)
3. [jobs.ashbyhq.com/modal](https://jobs.ashbyhq.com/modal)
4. [linkedin.com/company/modal-labs](https://www.linkedin.com/company/modal-labs)
5. [jobsbyculture.com/blog/working-at-modal-2026](https://jobsbyculture.com/blog/working-at-modal-2026)

## Other roles at Modal

- [Member of Technical Staff - SDK (Stockholm)](https://feeny.ai/job/member-of-technical-staff-sdk-stockholm-modal-stockholm-ks7rt2xw6atf) — Stockholm, Sweden
- [Talent Partner, Technical](https://feeny.ai/job/talent-partner-technical-modal-new-york-pyx4hkbcrzcb) — New York, NY
- [Talent Partner, GTM](https://feeny.ai/job/talent-partner-gtm-modal-san-francisco-1nm18692f4w7) — San Francisco, CA
- [Member of Technical Staff - Inference Runtime](https://feeny.ai/job/member-of-technical-staff-inference-runtime-modal-san-francisco-cvzk20vkphfy) — San Francisco, CA
- [Legal Operations Lead](https://feeny.ai/job/legal-operations-lead-modal-new-york-s5y3gn1e961k) — New York, NY
- [Senior Commercial Counsel](https://feeny.ai/job/senior-commercial-counsel-modal-new-york-grh0fxqa82by) — New York, NY
- [Field Marketing Manager](https://feeny.ai/job/field-marketing-manager-modal-san-francisco-0apep5s9tyq6) — San Francisco, CA
- [Global Workplace Lead](https://feeny.ai/job/global-workplace-lead-modal-new-york-4vjzkq1xtb4h) — New York, NY
- [Analytics Engineer](https://feeny.ai/job/analytics-engineer-modal-new-york-6t3v0rpsnf2z) — New York, NY
- [Member of Technical Staff - Lead, Storage](https://feeny.ai/job/member-of-technical-staff-lead-storage-modal-san-francisco-3wd2wad5dheq) — San Francisco, CA