AI Performance Optimization Engineer

Jobgether • United States

Remote

This Job is No Longer Active This position is no longer accepting applications

AI Summary

Jobgether is seeking an AI Performance Optimization Engineer to improve the performance, efficiency, and scalability of AI training and inference systems. The ideal candidate has deep expertise in AI systems, performance engineering, and large-scale distributed computing environments. This role requires strong programming skills in Python and C++, hands-on experience optimizing deep learning workloads on modern GPU architectures, and a deep understanding of distributed training, inference systems, and model parallelism techniques.

Key Highlights

Improve AI system performance and efficiency

Optimize distributed training and inference

Develop advanced model optimization techniques

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Key Responsibilities

Improve the performance, efficiency, and scalability of AI training and inference systems

Profile and optimize end-to-end AI pipelines

Identify bottlenecks across compute, memory, networking, and data pipelines

Technical Skills Required

Python C++ GPU architectures Distributed training Inference systems Model parallelism techniques Quantization Sparsity Pruning Compression Triton XLA TorchInductor TVM

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Benefits & Perks

Competitive full-time compensation

Fully remote work model

Long-term, stable engineering engagement

Opportunity to work on cutting-edge AI infrastructure challenges

Job Description

This role focuses on pushing the limits of performance for large-scale AI systems, with an emphasis on maximizing throughput, minimizing latency, and reducing operational costs across training and inference workloads. You will work across the full stack of AI infrastructure, from GPU-level kernel optimization to distributed system tuning and model serving architecture. The environment is highly technical, data-driven, and collaborative, involving close partnership with ML engineers, platform teams, and product stakeholders. You will help translate complex, ambiguous performance challenges into measurable engineering improvements. The role is ideal for a hands-on expert who thrives in deep systems work and production-grade optimization. You will also contribute to shaping standards, benchmarks, and best practices across AI infrastructure teams.

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