Estimated based on role seniority, company stage (Series B), and industry benchmarks. Actual compensation may vary.
Based on Web3 & AI industry compensation data. Seniority is inferred from role title keywords. Company stage affects ranges: early-stage (−15%), late-stage/public (+10%).
Modal provides the infrastructure foundation for AI teams. With instant GPU access, sub-second container startups, and native storage, Modal makes it simple to train models, run batch jobs, and serve low-latency inference. We have thousands of customers who rely on us for production AI workloads, including Lovable, Scale AI, Substack, and Suno.
We're a fast-growing team based out of NYC, SF, and Stockholm. We've hit 9-figure ARR and recently raised a Series B at a $1.1B valuation. Our investors include Lux Capital, Redpoint Ventures, Amplify Partners, and Elad Gil.
Working at Modal means joining one of the fastest-growing AI infrastructure organizations at an early stage, with many opportunities to grow within the company. Our team includes creators of popular open-source projects (e.g. Seaborn, Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
We're looking for strong backend engineers who love building a developer tools used by the largest AI companies in the world. You’ll be building for things at scale, but also for new AI workflows that change every day.
Experience building and shipping modern web applications end-to-end. We care more about what you’ve built than how many years you’ve been building.
Comfort working across the stack: TypeScript on the frontend, Python services on the backend, and ClickHouse for data and analytics.
Deep knowledge of observability tools and patterns used for large-scale workloads such as custom sandboxes, training and inference for large language (LLM) and diffusion models.
Experience with at least one of: billing/payments systems, B2B SaaS tooling, or enterprise software, or LLM / diffusion models inference and training loads.
Strong product instincts; you think about customer problems, not just tickets.
Ability to make good tradeoffs between shipping fast and building for scale.
Ability to work in-person in our NYC office.
Serverless cloud infrastructure for AI/ML workloads.
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