GranicaMountain View, CAResearch Engineer building scalable ML systems and pipelines for Large Tabular Models on enterprise structured data. Implement algorithms, optimize training/inference, develop benchmarks, and translate research ideas (led by Stanford Prof. Andrea Montanari) into production.
Characterize, analyze, and optimize performance of state-of-the-art AI models on Cerebras' wafer-scale hardware. Build performance models, optimize kernels and compilers, debug runtime behavior, and develop visualization tools to influence next-gen AI architecture.
Research Engineer on OpenAI's Privacy team designing and prototyping privacy-preserving ML algorithms like differential privacy and federated learning at scale. Requires hands-on PETs experience, fluency in PyTorch/JAX, and a track record implementing or publishing novel privacy work.
Research Engineer building self-improving AI agent systems at Console. Develop eval/optimization loops, fine-tune specialist models, and improve agent reasoning over enterprise context using production data to drive measurable gains in quality, latency, and reliability.
Build and scale the shared AI platform foundations at Notion, enabling fast and safe shipping of AI products. Requires experience with LLM/ML platforms, strong ownership, and comfort across backend, infrastructure, and product code.
Machine Learning Engineer building statistical models, optimization systems, and experiments for mobile ad tech economics on the Revenue Engine team. Requires PhD in CS/ML/Economics and industry experience applying ML or economics at scale.