Applied Intuition
AI Performance Engineer
$215K–$285K
Sunnyvale · On-site · Full-time · Posted Aug 12, 2026
- Experience
- 1 or more years of hands-on ML performance engineering experience; 5 or more years of overall experience.
- Education
- Bachelor's degree or higher in computer science or adjacent fields.
About the job
Optimizes distributed machine-learning training and high-throughput batch inference to improve accelerator utilization, cluster throughput, and processing cost.
What you'll do
- Profile and optimize distributed training, including data pipelines, kernels, communication, and checkpointing.
- Improve large-scale offline inference and multi-node scaling across petabyte-scale autonomy logs.
- Build performance models, benchmarks, observability, and regression-detection tools to improve cluster goodput.
What they ask for
- Bachelor's degree or higher in computer science or an adjacent field.
- At least 1 year of hands-on ML performance engineering experience and at least 5 years of overall experience.
- Experience with distributed multi-node training at scale and GPU or accelerator performance concepts.
- Experience with high-throughput or batch inference systems; fluency in Python and proficiency in C++ or another systems language.
Nice to have
- GPU kernel development or profiling toolchain experience.
- Experience with GPU orchestration, fault-tolerant training, or autonomy and robotics data.
Pay
$215K – $285K USD base salary
Tools
Python, C++, FSDP, DeepSpeed, Megatron, NCCL, NVIDIA Triton Inference Server, TensorRT
AI focus
Performance engineering for distributed ML training and high-throughput batch inference, including processing autonomy data for auto-labeling, data mining, ground-truth generation, and evaluation.
Model-written summary · the original posting is authoritative