AI Researcher — Inference Optimization

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Quick Summary

Overview

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.

Key Responsibilities

Research and develop techniques to optimize inference performance for large neural networks. Improve latency, throughput, memory efficiency, and cost per inference.

Requirements Summary

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).

Technical Tools

pythonpytorchdeep-learningmachine-learning

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.

Responsibilities

~1 min read

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Research and develop techniques to optimize inference performance for large neural networks.
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Improve latency, throughput, memory efficiency, and cost per inference.
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Design and evaluate model-level optimizations (quantization, pruning, KV-cache optimization, architecture-aware simplifications).
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Implement systems-level optimizations (dynamic batching, kernel fusion, multi-GPU inference, prefill vs decode optimization).
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Benchmark inference workloads across hardware accelerators.
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Collaborate with engineering teams to deploy optimized inference pipelines.
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Translate research insights into production-ready improvements.

Requirements

~1 min read

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.

Requirements

~1 min read

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).