AI Acceleration Algorithm Engineer

About the role

We are looking for an AI Acceleration Algorithm Engineer to research, design, and productionize algorithms that improve end-to-end AI compute, retrieval, storage, and inference efficiency. The role emphasizes independent algorithmic innovation and measurable system-level benefits across accuracy, throughput, latency, memory capacity, and cost.

What you will do

• Design inference optimization algorithms for dynamic batching, request scheduling, parallelism, distributed sharding, load balancing, and elastic resource allocation.
• Research model-efficiency techniques including post-training quantization, quantization-aware training, low-precision inference, structured sparsity, pruning, and distillation.
• Develop high-performance retrieval, vector-search, data-placement, storage-caching, and I/O optimization algorithms for AI workloads.
• Build cost models, search strategies, and adaptive optimization algorithms that balance accuracy, throughput, latency, memory capacity, and operating cost.
• Productionize algorithms in training or inference systems and evaluate them using reproducible baselines, ablations, and end-to-end metrics.

What we are looking for

• Master’s degree or Ph.D. in Computer Science, AI, High-Performance Computing, Operations Research, or a related field.
• Representative work in one primary area and meaningful experience in at least one adjacent area: retrieval, AI storage optimization, inference scheduling and acceleration, quantization, sparsification, or AI compute-platform optimization.
• Evidence of an independent algorithmic contribution, rather than framework integration or parameter tuning alone.
• Proficiency in C++/Python, Linux, CUDA or equivalent accelerator development, and mainstream frameworks such as PyTorch, TensorRT-LLM, vLLM, SGLang, or Megatron-LM.
• Quantified results using metrics such as accuracy, throughput, TTFT, TPOT, latency, memory footprint, scalability, or cost.

Preferred qualifications

• Production deployment at significant model, traffic, data, or cluster scale.
• Strong publications, patents, open-source contributions, or benchmark results in a relevant area.