Founding Data Scientist, Capacity & Pricing Optimization

1 нед. назад

250k–300k USD / годСШАSeniorHybrid

pythoncausal inferencedynamic pricing

Build and own quantitative models for pricing, packaging, and capacity allocation to optimize AI GPU resource utilization.

is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and backed by AMD, Atreides, Benchmark Capital, Index Ventures, Lightspeed, NVIDIA, Sequoia Capital, and TCV, powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that's building the future of enterprise AI.
Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving.
Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally.
Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results.
Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation.
AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.

GPUs are the most expensive and supply-constrained input in AI. Our ability to effectively deploy them towards our customer demand is one of the critical inputs to our growth. We're hiring our first Data Scientist to build and own the quantitative models that drive pricing, packaging, and capacity allocation in order to optimize capacity and balance supply with demand.
The ideal candidate brings expertise at the intersection of operations research and economics with the ability to both model our market, build dynamic pricing models, and design experiments and causal studies. This is a hands-on role: you'll write production-quality code, ship models that drive real decisions, and bring an true ownership mentality to help set priorities in addition to executing on them.

Master’s degree in Operations Research, Economics, Industrial Engineering, Statistics, or a related quantitative field with 7+ years of hands-on experience in pricing, revenue management, or optimization. PhD preferred.
Strong grounding in at least two of: stochastic optimization, revenue management and pricing, causal inference and experimental design, or combinatorial optimization.
A track record of delivering production quality models that drive direct business impact.
Strong coding skills and experience with optimization tooling & methodologies.
Ability to communicate tradeoffs and uncertainty clearly to engineers and executives.

Experience on capacity planning, forecasting, or pricing science teams at a cloud computing provider or marketplace business.
Familiarity with GPU infrastructure and LLM inference economics (throughput, batching, utilization, cost per token).
Experience as the first quantitative hire in a new function, building models and data pipelines from scratch.

Build fill-rate models for serverless inference, balancing utilization against performance and latency targets.
Model how traffic is shared and multiplexed across models and customers to set safe capacity buffers.
Design pricing for low-utilization capacity, including spot and preemptible offerings, off-peak and overnight windows, and sub-scale capacity pools.
Develop pricing policies to optimize our capacity utilization and price-yield.
Design execute experiments and causal studies to estimate elasticity across customer segments, models, and products.
Optimize the model-hardware matching across GPU generations, vendors, clouds, and regions, accounting for workload fit, substitution, and lead times.
Optimize our capacity across commitment types and terms, quantifying the tradeoff between cost, flexibility, and utilization risk to guide how we source capacity.