Staff Software Engineer - AI Infrastructure

About the Role

Key Responsibilities

• Design, develop, and maintain high-performance Dataloader SDKs and Dataset Management Systems for multi-source, heterogeneous data (images, videos, point clouds, sensor streams, etc.).
• Optimize multi-threaded/multi-process data pipelines for minimal I/O latency and preprocessing overhead, supporting large-scale model training and inference workloads.
• Contribute to AI infrastructure projects beyond data loading, including distributed training and inference optimization.
• Develop custom operators (CUDA kernels, TensorRT, ROCm) and hardware-specific acceleration for GPU/TPU.
• Implement model optimization techniques such as pruning, quantization, distillation, sparsification, and mixed-precision training.
• Collaborate with algorithm and platform teams to translate business needs into scalable, production-grade solutions.
• Continuously identify and address performance bottlenecks across the AI training and inference stack.

Requirements

• Master's degree in Computer Science, Software Engineering, or equivalent experience.
• 5+ years of experience in large-scale data processing or ML infrastructure.
• Proficient in Python with solid software engineering fundamentals, clean coding practices, and strong debugging skills.
• Hands-on experience with relational databases and NoSQL systems, including metadata and cache management; prior experience with large-scale VectorDB is highly desirable.
• Experience in at least one of the following areas: large-scale deep learning training or inference optimization (distributed training strategies, quantization, CUDA kernel development), columnar storage formats (Parquet/ORC), Linux file system and network I/O optimization, large-scale data loading frameworks (PyTorch Dataloader, Hugging Face Datasets).
• Strong communication skills and ability to work cross-functionally in fast-paced environments.
• Ability to learn quickly, adapt to new challenges, and proactively explore and adopt new technologies.

Nice to Have

• Familiarity with the autonomous driving industry and enthusiasm for its challenges.
• Experience with distributed computing frameworks such as Apache Ray.
• Experience in building and scaling ML infrastructure in cloud-native environments.

Qualifications

• Master's degree in Computer Science, Software Engineering, or equivalent experience.

Benefits & Perks

• Base salary range of $179,400-$303,600, in addition to bonus, equity, and benefits.