Senior Applied Research Engineer
Core
Building and optimizing large-scale foundation models (Large Tabular Models) for enterprise decision-making, focusing on distributed training efficiency and productionization.
Role type
Senior Applied Research Engineer (ML Infrastructure & Optimization)
Builds
Large Tabular Models (LTM) and their training infrastructure for Fortune 100 companies
Domain
Enterprise AI, Foundation Models, Distributed Systems
Deliverable
production ML models
Required skills
Distributed training pipelines, Multi-GPU systems, Profiling and debugging, Python, Model scaling and parallelization
Preferred skills
NCCL, MPI, PyTorch internals, C++, CUDA
Technologies
Triton, CUDA, NCCL, MPI, PyTorch
Responsibilities
Profile end-to-end distributed training runs to identify bottlenecks, Contribute to architectural decisions for large-scale training jobs, Design and implement model scaling and memory optimization techniques, Collaborate with ML Researchers to ensure research ideas scale efficiently, Drive productionization and serving of models including inference optimization
Seniority
Senior, hands-on IC
