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Neural Concept

3D deep-learning CAD-CAE design optimization

Platform Deployment Engineer

Implementation & Technical Account ManagementUpdated on Oct 30
Full-timeJersey City, NJ, USA

About the Job

Requirements

  • At least 6 years of experience deploying and operating enterprise software in production, including 2 years in environments the candidate did not control.
  • Configuration-level Kubernetes knowledge, including how chart values become rendered manifests and diagnosing why a workload will not start.
  • Understanding of identity and single sign-on fundamentals, including OpenID Connect and Security Assertion Markup Language, clients and credential rotation, redirect URIs, and group-to-role mapping.
  • Knowledge of Linux, networking, and Transport Layer Security, including DNS, routing, ingress and egress, certificate chains, and interception proxies.
  • Knowledge of Kubernetes storage, including persistent volumes and Container Storage Interface, access modes, and diagnosing shared storage degradation under load.
  • Experience personally carrying and supporting a production deployment; candidates should be able to name and date it and describe what went wrong.
  • Professional customer-facing English proficiency.
  • Willingness to travel and work customers’ hours.

Responsibilities

  • Own customer deployment outcomes end to end for named accounts, from the first architecture conversation through go-live and steady-state operation.
  • Integrate the platform with customer identity, data, and engineering systems, as well as the underlying infrastructure and model-serving layer.
  • Manage technical relationships with customer IT, security, and platform teams, including assessments, architecture reviews, and responding when issues occur.
  • Diagnose operational issues, including causes outside the company’s software.
  • Create runbooks, post-mortems, and playbooks to improve subsequent deployments and feed learnings back to Product.
  • Deploy and operate across managed Kubernetes on major cloud platforms, on-premises and fully air-gapped clusters, GPU infrastructure, self-hosted model serving, and customer high-performance computing environments, schedulers, and shared storage.

Desired Qualifications

  • Experience with regulated or air-gapped deployments.
  • Experience with private registries and image mirroring.
  • Experience with GPU scheduling and large language model serving.
  • Enterprise identity provider administration experience.
  • Experience with Terraform and GitOps.
  • Experience with Prometheus and Grafana.
  • Experience with high-performance computing schedulers.
  • Experience with ISO 27001 evidence.
  • Python skills.
  • Exposure to product lifecycle management, computer-aided design, or computer-aided engineering.

About the Company

Neural Concept

3D deep-learning CAD-CAE design optimization

Neural Concept uses 3D deep-learning to improve product design in automotive and aerospace. Its product, Neural Concept Shape (NCS), analyzes designs directly within CAD to provide physics-based insights, guiding designers without the need for meshing and lengthy simulations. This speeds up the design workflow by bridging CAD and CAE teams, reducing iteration time and late-stage failures. The company licenses its technology to clients, integrating into existing design processes, and has partnered with OEMs and Tier 1 suppliers such as Danfoss and Mitsubishi Chemical Group to boost performance and efficiency. Its goal is to help customers cut design lead times by substantial margins (claimed over 90%) and deliver better-performing products faster.

Company size
51-200
Company stage
Series C
Total funding
$137.8M
Headquarters
Lausanne, Switzerland
Founded
2018

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