Senior Applied Research Engineer

About the Company

Fundamental is an AI company pioneering the future of enterprise decision-making. Founded by DeepMind alumni, Fundamental has developed NEXUS – the world's most powerful Large Tabular Model (LTM) – purpose-built for the structured records that actually drive enterprise decisions.

Responsibilities

• Profile end-to-end distributed training runs to identify bottlenecks across compute, GPU memory, and inter-GPU communication
• Contribute to architectural decisions that improve the efficiency and reliability of large-scale training jobs, including developing Triton/CUDA kernels when needed
• Design and implement model scaling, parallelization, and memory optimization techniques for training workloads with very large context sizes
• Collaborate closely with ML Researchers to diagnose architectural inefficiencies and ensure research ideas scale efficiently
• Drive the productionization and serving of models, including improving inference efficiency through quantization

Requirements

• Strong understanding of modern ML architectures and large-scale training pipelines
• Experience running distributed training jobs on multi-GPU systems
• Advanced profiling and debugging skills across CPU, GPU, memory usage, latency, and inter-GPU communication
• Strong programming skills in Python
• Experience with model scaling and parallelization strategies, including tensor and pipeline parallelism

Preferred Qualifications

• Familiarity with NCCL, MPI, and distributed communication primitives
• Knowledge of PyTorch and Triton internals
• Programming experience with C++ and CUDA

Benefits

• Competitive compensation with salary and equity
• Comprehensive health coverage for you and your dependents
• Paid parental leave for all new parents
• Relocation support for employees moving to join the team