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, Ramp, 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 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,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
The Role
We're looking for an Engineering Manager to lead a team of highly experienced engineers building the infrastructure that powers Modal's serverless GPU platform. This is a hands-on leadership role — expect to split your time between technical contribution and people management depending on what the team needs. You'll set direction, remove blockers, and build a strong engineering culture as your team tackles hard problems in distributed computing, large-scale data handling, and performance optimization.
Who You AreYou're an experienced engineering leader who stays close to the work and builds alongside your team when it counts. You earn trust through technical depth, not title. You communicate clearly, help strong engineers move fast without cutting corners, and stay calm and pragmatic under pressure. You care as much about how your team gets to an answer as the answer itself.
ResponsibilitiesTeam
Recruit, hire, and grow a high-performing team of cloud platform engineers; run regular 1:1s focused on coaching, feedback, and career growth.
Set clear performance expectations, hold a high bar, and build an environment where engineers do their best work.
Foster a culture of ownership, accountability, and continuous improvement.
Technical Direction
Drive day-to-day technical decisions through design reviews, code reviews, and architectural discussions.
Translate the infrastructure roadmap into clear team priorities and milestones, and hold execution against them.
Establish standards for reliability, performance, and operational excellence; ensure the team owns projects end-to-end, from spec through production.
Push for good judgment on tooling and architecture, with a bias against unnecessary complexity.
Cross-Functional & Incident Leadership
Partner with product and engineering to align infrastructure work with business priorities; represent your team's progress, capacity, and tradeoffs clearly to leadership.
Serve as the escalation point for major incidents; drive resolution with urgency and ensure the team learns systematically, feeding those learnings back into infrastructure improvements.
10+ years of industry experience, including 3+ years in a leadership role
Track record building high-performance distributed systems at scale
Strong background in cloud infrastructure
Deep knowledge of low-level OS foundations (Linux kernel, file systems, containers, etc.)
Proficiency in a systems-level language (Rust, C, C++, or Java)
Skills Required
- At least 10 years of industry experience
- At least 3 years experience in a leadership role
- Experience building high-performance distributed systems at a large scale
- Strong background in cloud infrastructure
- Strong knowledge of low-level operating system foundations (Linux kernel, file systems, containers, etc.)
- Proficient in systems-level languages such as Rust, C, C++, or Java
What We Do
Deploy generative AI models, large-scale batch jobs, job queues, and more on Modal's platform. We help data science and machine learning teams accelerate development, reduce costs, and effortlessly scale workloads across thousands of CPUs and GPUs. Our pay-per-use model ensures you're billed only for actual compute time, down to the CPU cycle. No more wasted resources or idle costs—just efficient, scalable computing power when you need it.
