Field Solutions Engineer, AI & Emerging Workflows (Aerospace and Defense)

US Remote

Sales

Remote

Full-time

About nTop

At nTop, we are passionate about building next-generation engineering solutions. We enable the most innovative engineering companies in the world to transform how they develop, test, and manufacture better products faster. Our software has been used to engineer mission-critical satellites, deliver stronger and lighter medical implants for patients in need, and allow engineers to create complex, high-performance products never before possible.

The Role

Our team is searching for a Field Solutions Engineer with a strong background in aircraft conceptual or preliminary design to join our Field Solutions Engineering team. Our most active strategic customers are in Aerospace & Defense, designing air vehicles.

In this role, you’ll embed with strategic customers and technical partners to build nTop workflows, design automation, and proofs of concept against problems scoped by the engagement lead. Increasingly, that means connecting nTop into AI-embedded and agentic engineering workflows: headless automation, large-scale DOE and parameter sweeps, surrogate models, and agent-callable toolchains developed alongside our AI and GPU-compute partners. You’ll collaborate closely with Partnerships, Account Executives, Customer Success, and Product. As engagements conclude, you’ll move to new strategic engagements based on business need.

This role is remote and reports into the Senior Manager, Field Solutions Engineering.

What You Will Do

Strategic Project Management

Field Solutions Engineering is a Forward Deployed Engineering team, and hires should expect frequent travel to customer locations across the US to execute design sprints in on-site collaboration with our partner customers. Engagements will regularly involve Aerospace & Defense programs and may include longer-term, on-site collaboration.

• Own technical project components within strategic customer and partner engagements, taking direction from the engagement lead
• Deliver to a clear definition of done, and surface risks and blockers early

Focused Domain Expertise

1. Engagement Delivery

• Build nTop workflows, design automation, and proofs of concept against real customer data, including headless and batch execution for DOE, parameter sweeps, and surrogate-model training data
• Write Python tooling and integrations that connect nTop to customer toolchains such as CAD, FEA/CFD, MDO frameworks, and agentic frameworks (e.g., MCP-style tool interfaces)
• Help shape the architecture of emerging workflows: how nTop, simulation, data, compute, and AI agents connect end to end, and what it takes to move a proof of concept into production
• Use AI-assisted development and agentic tools as part of your normal workflow, and feed what works back to the team and Product

2. Pre-Sale Support

• Support the technical sales process through discovery, demonstration, and proof of concept development
• Deliver tailored product demos aligned with customer use cases, including AI-embedded and agentic workflow demos

3. Post-Sale Workflow Consultation

• Lead technical sessions such as solution reviews and scoping calls to help customers expand usage and realize value
• Resolve workflow consultation requests across design automation and workflow development

Required Experience

• A Bachelor’s and/or Master’s degree in Mechanical, Aerospace, Computational Engineering, or a related engineering discipline, plus 3+ years of industry experience (highly relevant doctoral degrees can be substituted for three years)
• Working knowledge of the aerospace design and analysis toolchain, including at least one of: parametric CAD (CATIA, Siemens NX, SolidWorks, Onshape); FEA (Nastran, Abaqus, Ansys, LS-DYNA); CFD (Fluent/CFX, Star-CCM+, OpenFOAM); conceptual design and MDO tools (OpenVSP, OpenMDAO, ModelCenter, HEEDS, Isight); or PLM (Teamcenter, 3DEXPERIENCE, Windchill)
• Experience scripting in Python, including tools or integrations used by other engineers
• Familiarity with the scientific Python stack (NumPy, SciPy, pandas), LLM APIs, agent frameworks, or tool-calling protocols (e.g., MCP), or at least one ML framework
• Demonstrated ability to learn new technical software quickly; prior experience with nTop, parametric CAD, or computational design tools is valuable
• Systems thinking: able to map how geometry, simulati…