AI Researcher — Inference Optimization

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About this role

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).