AI Researcher — Inference Optimization

Role Overview We are seeking an AI Researcher with deep experience in inference optimization to design, evaluate, and deploy high-performance inference systems for large-scale machine learning models. You will work at the intersection of model architecture, systems engineering, and hardware-aware optimization , improving latency, throughput, and cost efficiency across real-world production environments. Key Responsibilities - Research and develop techniques to optimize inference performance for large neural networks. - Improve latency, throughput, memory efficiency, and cost per inference . - Design and evaluate model-level optimizations (quantization, pruning, KV-cache optimization, architecture-aware simplifications). - Implement systems-level optimizations (dynamic batching, kernel fusion, multi-GPU inference, prefill vs decode optimization). - Benchmark inference workloads across hardware accelerators. - Collaborate with engineering teams to deploy optimized inference pipelines . - Translate research insights into production-ready improvements . Required Qualifications - Strong background in machine learning, deep learning, or AI systems . - Hands-on experience optimizing inference for large-scale models . - Proficiency in Python and modern ML frameworks (e.g., PyTorch). - Experience with inference tooling (e.g., Triton, TensorRT, vLLM, ONNX Runtime). - Ability to design experiments and communicate results clearly. Preferred / Nice-to-Have Qualifications - Experience deploying production inference systems at scale . - Familiarity with distributed and multi-GPU inference . - Experience contributing to open-source ML or inference frameworks . - Authorship or co-authorship of peer-reviewed research papers in machine learning, systems, or related fields. - Experience working close to hardware (CUDA, ROCm, profiling tools). What Success Looks Like - Measurable gains in latency, throughput, and cost efficiency . - Optimized inference systems running reliably in production. - Research ideas successfully translated into deployable systems. - Clear benchmarks and documentation that inform product decisions. Relevant Research Areas (Bonus) - Long-context inference optimization - Speculative decoding - KV-cache compression and paging - Efficient decoding strategies - Hardware-aware inference design Originally posted on Himalayas