---
title: 'Member of Technical Staff - Storage (Stockholm) at Modal'
canonical: 'https://feeny.ai/job/member-of-technical-staff-storage-stockholm-modal-stockholm-24yjjh3wj6n2'
type: 'job'
last_seen: '2026-11-03'
---

# Member of Technical Staff - Storage (Stockholm) at Modal

- **Company:** Modal
- **Location:** Stockholm, Sweden
- **Employment:** full-time
- **Posted:** 2026-10-14
- **Last confirmed live:** 2026-11-03
- **Apply:** https://jobs.ashbyhq.com/modal/34858ff6-aecf-4c58-9574-ed67ebf700c8

## 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 [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've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation.

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 are looking for strong engineers with experience and interest in designing, building, and maintaining the novel, high-performance systems that make up our serverless platform. Specifically, you’ll be working on the distributed object storage system that underpins every container image, volume, and checkpoint on Modal: hundreds of petabytes of data, replicated across multiple cloud object stores and a CDN, cached on local NVMe across a large fleet of workers in many datacenters, and shared peer-to-peer within each datacenter. You’ll make cold starts feel local when the data is hundreds of milliseconds away, designing the caching, preloading, and peer-to-peer layers that hide object-store latency and keep public ingress off saturated uplinks. You’ll own durability and cost at petabyte scale, from streaming and batch replication between origins to garbage collection over billions of objects. You’ll work across the stack, from local disk and page cache to distributed blob storage and garbage collection, and you’ll help shape what storage becomes next as we push storage closer to workloads.

What You’ll Work On:

- Build P2P sharing of data across workers within a single datacenter to dramatically reduce ingress
- Replicate data across multiple blob storage providers
- Automate garbage collection across hundreds of petabytes of data
- Deploy colocated storage clusters to datacenters to accelerate high-throughput customer workloads

You May Be a Fit If:

- You have 5+ years of experience writing high-quality production code
- You’ve built high-performance distributed storage or caching systems at a large scale (the more challenges you’ve worked through, the better)
- You have strong cloud skills, including deep familiarity with object storage (S3 or similar), CDNs, and their consistency, throughput, and cost characteristics
- You have strong knowledge of low-level operating system foundations (Linux kernel, file systems, page cache, containers, etc.)
- You’re willing to step into the thick of it with our on-call rotation and respond to production incidents

Nice-to-Have:

- Experience with replication, content addressing, and consistency models in multi-region or multi-cloud systems
- Experience operating storage systems at scale (petabyte-scale datasets, high-throughput read/write paths, large-scale garbage collection or data migration)
- Experience with data engineering at petabyte scale
- Prior experience with Rust

## 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

- [Talent Partner, Technical](https://feeny.ai/job/talent-partner-technical-modal-new-york-9gqq77qqdag1) — New York, NY
- [Head of IT](https://feeny.ai/job/head-of-it-modal-new-york-a8w16t8ptzer) — New York, NY
- [GTM Engineer - Data Platform](https://feeny.ai/job/gtm-engineer-data-platform-modal-new-york-kjkbrxkqfe7z) — New York, NY
- [Member of Technical Staff - SDK (Stockholm)](https://feeny.ai/job/member-of-technical-staff-sdk-stockholm-modal-stockholm-ks7rt2xw6atf) — Stockholm, Sweden
- [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