Inference & Workload Optimization Lead – Horizon AI

Job not on LinkedIn

🕒 October 21

🇺🇸 United States – Remote

⏰ Full Time

🟠 Senior

🤖 Artificial Intelligence

👻 Ghost score 17%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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STN Incorporated

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2016

🏢 Enterprise

🔒 Cybersecurity

🔧 Hardware

Enterprise • Cybersecurity • Hardware

STN Incorporated is an enterprise-grade managed IT and cloud infrastructure provider that delivers secure, audit-ready infrastructure for business-critical systems and demanding AI workloads. STN operates a managed operating model offering private CPU clouds, GPU One AI infrastructure, secure networking and storage, and 24/7 human support with high uptime SLAs. Their services include managed infrastructure and cloud operations, cybersecurity operations and incident response, compliance and risk management (SOC 2 Type II, HIPAA-ready), backup and recovery, and enterprise technology procurement and lifecycle management. STN serves enterprises, high-growth SaaS companies, AI builders and model developers, robotics/physical AI firms, and regulated industries such as healthcare.

📋 Description

• Profile customer training and inference workloads to identify bottlenecks in data loading, collective communication, memory bandwidth, kernel efficiency, batching, and parallelism layout • Establish performance baselines and define good performance for each hardware generation, starting with current-generation NVIDIA hardware • Convert optimization expertise into agent-executable fixes applicable across customer clusters • Work with committed-capacity customers, directly or through operating teams, on workload tuning • Own internal model serving as a secondary responsibility, including cost- and latency-aware routing and model version migration • Advise on GPU allocation between internal inference and customer workloads

🎯 Requirements

• Deep GPU performance engineering, including profiling, memory and bandwidth behaviour, and kernel-level understanding • Serving stack depth with technologies such as vLLM, SGLang, or TensorRT-LLM, including batching, KV cache management, and quantization trade-offs • Distributed training and inference experience at scale, including tensor, pipeline, and data parallelism • Experience tuning collective communication such as NCCL • Fluency with profiling tools such as Nsight, DCGM, and framework-level profilers • Ability to explain performance findings to customers and encode them for an agent • Nice to have: CUDA kernel work, open-weight model deployment at scale, benchmark design, and prior customer-facing technical experience

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