Software Engineer, Data Platform Lead – Horizon AI

Job not on LinkedIn

🕒 October 21

🇺🇸 United States – Remote

⏰ Full Time

🟠 Senior

🚰 Data Engineer

👻 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

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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

• Design and build the fleet knowledge model covering assets, topology, configuration, and current state • Build the decision-capture pipeline and instrument operator workflows to collect labelled human decisions • Own the data access layer abstracting telemetry sources • Negotiate telemetry requirements with owning teams, including signals, granularity, retention, and raw event access • Own the scale path, including vector index behaviour and analytics separation for a growing schema • Report to the Chief AI Officer

🎯 Requirements

• Strong data modelling judgment, particularly modelling physical or infrastructure domains as something queryable • Experience with time-series and event data at scale, including cardinality and retention trade-offs • Depth in PostgreSQL, including vector search, and comfort with a large evolving schema • Enough infrastructure literacy to understand nodes, GPUs, fabric, schedulers, and power • Familiarity with DCIM tooling is a strong plus • Bias toward shipping a usable model early over designing a complete one • Nice to have: GPU fleet or HPC exposure, DCGM, Slurm, and entity resolution across inconsistent sources

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