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AI Researcher — Inference Optimization

🌏 Worldwide

CUDA

Python

Machine Learning

Design

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AI Researcher — Inference Optimization

from 🌏 Worldwide

Role Overview

We are seeking anAI 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 ofmodel architecture, systems engineering, and hardware-aware optimization, improving latency, throughput, and cost efficiency across real-world production environments.

Key Responsibilities

Research and develop techniques tooptimize inference performance for large neural networks.
Improvelatency, throughput, memory efficiency, and cost per inference.
Design and evaluatemodel-level optimizations (quantization, pruning, KV-cache optimization, architecture-aware simplifications).
Implementsystems-level optimizations (dynamic batching, kernel fusion, multi-GPU inference, prefill vs decode optimization).
Benchmark inference workloads across hardware accelerators.
Collaborate with engineering teams todeploy optimized inference pipelines.
Translate research insights intoproduction-ready improvements.

Required Qualifications

Strong background inmachine learning, deep learning, or AI systems.
Hands-on experience optimizing inference forlarge-scale models.
Proficiency inPython 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 deployingproduction inference systems at scale.
Familiarity withdistributed and multi-GPU inference.
Experience contributing toopen-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).