About Us:
Modal is building the serverless compute platform to support the next generation of AI companies. In order to deliver the developer experience we wanted, we went deep and built our own infrastructure—including our own custom file system, container runtime, scheduler, container image builder, and much more.
We're a small team based out of New York, Stockholm and San Francisco. In just one year, we've reached 8-figure revenue, tripled our headcount, scaled to support thousands of GPUs, and raised over $32M in funding.
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.
The Role:
Modal is looking for something that lacks a conventional title but could roughly be described as a "Cloud Quant" or "Coding CFO".
The background is that Modal spends millions of dollars every month on cloud costs, and there's a tremendous amount of opportunities to optimize these costs:
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Engage with new cloud providers and get quotes
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Finding pockets of cheap capacity in existing cloud providers
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Improving GPU utilization
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Negotiating with existing vendors
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Pricing optimizations, picking the right trade-off between growth and revenue
This is a role that mixes vendor negotiations, data science, software engineering, and trading. We want people who are deep systems thinkers and love optimizing things. The actual work spans a very wide range of activities – both deep coding, but also lots of vendor management.
Besides what's mentioned above, the job will also entail things like:
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Reaching out to new potential vendors and get the best pricing
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Pulling data from various cloud APIs and analyzing data
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Downloading large datasets of dollar spend and analyze those
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Working with software engineers at Modal to roll out optimizations to how we manage our large GPU fleet
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Figure out new creative ways to improve our pricing, maybe by bundling similar GPU types, having "surge pricing", or discounts for nightly bath jobs.
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Reporting directly to the CEO
Requirements:
We think the ideal candidate has a mix of skills. In particular, we expect you:
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Have several years of Python and SQL experience
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Have done a fair bit of data science, data visualization, and some statistics
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Have experience working with cloud vendors (AWS etc)
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Love thinking about how to optimize stuff, especially the bottom line
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Don't mind getting on the phone with vendors and negotiate big contracts
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Love thinking about businesses as big complex systems
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Want to work out of our HQ in NYC (or for exceptional candidates, in SF)
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Love telling stories about how they made their previous company lots of money
Top Skills
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.
