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