Top Tech Jobs & Startup Jobs

Deploy and operate Neural Concept’s AI platform in enterprise customer environments, from architecture through go-live and steady-state support. Integrate identity, data, engineering systems, infrastructure, and model-serving layers across cloud, on-premises, air-gapped, GPU, and HPC environments. Partner with customer IT and security teams, troubleshoot cross-system issues, create runbooks and post-mortems, and feed deployment insights into Product. The role requires customer-facing communication, production deployment ownership, and travel to U.S. customer sites.

Build and ship a production capability connecting engineering designs with requirements, design rules, manufacturing and assembly constraints, and cost data. Develop Python software, LLM and agent pipelines, document extraction, enterprise integrations, traceable data models, evaluation metrics, and human-in-the-loop review workflows. Collaborate directly with product managers, solution engineers, and customer engineering teams from discovery through production deployment.

Own full-cycle go-to-market recruiting for sales, solution engineering, customer success, forward-deployed engineering, and marketing roles. Partner with hiring managers, source candidates using AI tools, manage pipelines and reporting in Ashby, deliver strong candidate experiences, close offers, and improve recruiting processes. The role focuses mainly on US hiring while supporting global searches across the US and Europe.

Own the full enterprise sales cycle for strategic engineering and AI software customers, from prospecting and qualification through negotiation, closing, and expansion. Develop territory and account strategies, build executive relationships, create value-based business cases, coordinate cross-functional teams, manage pipelines and forecasts, and lead complex sales involving engineering, IT, procurement, and senior executives. The role targets automotive, aerospace, defense, high-tech, electronics, and advanced manufacturing companies.

Implement customer projects using Python, machine learning, and deep-learning tools for engineering, physics, CAD, CAE, and manufacturing workflows. Develop proofs of concept, analyze project data, train customer teams, and translate customer feedback into product improvements. The role requires strong engineering and machine-learning knowledge, simulation experience, customer-facing communication, and travel to US customer locations.

Build technical prototypes with NVIDIA and other partner technologies, then turn them into credible demos, videos, articles, event content, and co-marketing assets. Translate complex engineering and AI concepts into clear, visually compelling stories for external audiences. Collaborate across Marketing, Product, Research, Engineering, Vertical Solutions, and partner teams while validating technical claims and prioritizing market relevance.

Design and deliver production-grade, Python-first AI workflows for engineering applications. Integrate CAD, CAE, and machine learning tools into the Neural Concept platform, owning coding, testing, packaging, CI, documentation, and releases. Develop reusable components, templates, and best practices while collaborating with product managers, CAD/CAE specialists, customers, and end users. Support roadmap definition, customer feedback, adoption, training, and technical architecture.

Lead technical pre-sales on strategic accounts across DACH & Nordics: run discovery, define AI transformation roadmaps, deliver client-tailored demos and Statements of Work, grow the regional SE team, and partner with Sales, FDE, Product and R&D to close and scale multi-year engagements.

Own enterprise customer deployments from architecture through go-live and steady-state operation. Integrate the platform with identity, data, engineering systems, infrastructure, and model-serving layers across cloud, on-premises, air-gapped, GPU, and HPC environments. Partner with customer IT and security teams, troubleshoot complex incidents, create runbooks and post-mortems, and provide deployment feedback to Product. Travel within Europe and support customer operating hours.

Own enterprise customer deployments from architecture through go-live and ongoing operations. Integrate the platform with identity, data, engineering, infrastructure, and model-serving systems across managed Kubernetes, cloud, on-premises, air-gapped, GPU, and HPC environments. Lead security reviews, troubleshoot complex infrastructure issues, support customers, create operational documentation, and feed deployment insights into Product.