Principal Software Engineer – Workload Orchestration, Distributed Compute

🔥 6 minutes ago

🌐 Germany, United Kingdom – Remote

🇩🇪 Germany – Remote 🇬🇧 United Kingdom – Remote

💵 $165k - $215k / year

⏰ Full Time

🔴 Lead

🧑‍💻 Full-stack Engineer

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nTop

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2015

🚀 Aerospace

⚡ Energy

☁️ SaaS

💰 Venture Round on 2024-09

Aerospace • Energy • SaaS

nTop is a software platform for engineering design automation. It generates and validates mission-ready geometry automatically from robust parametric/implicit models, enabling engineers to explore thousands of design variants quickly and reliably. The platform connects geometry generation to physics evaluation (user solvers or Physics AI), captures design logic for reuse across programs, and targets high-performance industries such as aerospace, propulsion, motorsports and energy. nTop is positioned as a tool to scale engineering throughput and move trade studies from estimates to physics-backed decisions.

📋 Description

• Own platform projects end to end, including scoping, task breakdown, execution, and delivery. • Extract subsystems from a C++/Python monolith behind clean interfaces. • Build paths for submitting and scheduling workloads on cloud or on-premises clusters. • Move parts of the file format toward an open, text-based structure. • Investigate, prototype, compare, and recommend solutions for open platform problems involving parallel simulation jobs across mixed on-premises and cloud GPU environments. • Coordinate cross-team dependencies and communicate how projects connect to other engineering work. • Improve tests, interfaces, and feedback loops. • Deliver scoped projects in the first three months, larger cross-team projects by month six, and productionized platform projects by month twelve.

🎯 Requirements

• 6+ years of professional software engineering, with a record of owning projects end to end and delivering across team boundaries. • Strong C++ experience building production systems; comfortable working in Python as well. • Hands-on experience with distributed or parallel systems, such as job scheduling, workload orchestration, cluster or cloud compute, or fault-tolerant services. • Experience improving a large, long-lived codebase step by step: refactoring, API design, or extracting modules without breaking existing users. • Comfort with ambiguity; ability to research options, weigh trade-offs, and make clear recommendations. • Clear written and spoken communication. • Experience helping teammates grow through code review, documentation, and pairing.

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