AI Infrastructure Engineer - Recommendation & LLM

What you'll do

• Design and implement scalable, reliable distributed training and serving systems.
• Optimize performance across GPUs, memory, networking, and runtime; improve latency, throughput, utilization, and cost efficiency.
• Productionize new model architectures and algorithms with researchers and algorithm engineers; lead technical design and rollout.

What they ask for

• Bachelor’s, Master’s, or Ph.D. in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience.
• 3+ years of software engineering, ML systems, distributed systems, or related industry experience.
• Strong C++ or Python programming skills and experience building production-quality software.
• Hands-on experience with PyTorch, TensorFlow, or other machine learning frameworks.

Nice to have

• Infrastructure experience for large-scale recommendation systems, LLMs, or foundation models.
• Experience with distributed training/model parallelism, GPU optimization, or LLM inference and serving techniques.

Tools

C++, Python, PyTorch, TensorFlow, CUDA, Triton, FSDP, ZeRO

AI focus

Develops and optimizes recommendation-model and LLM training and serving infrastructure, including distributed training, GPU optimization, and efficient inference.