Jobs at Orbital Industries

Lead the design and delivery of electrical systems for modular, high-density AI data centers. Responsibilities include electrical system architecture, load and resiliency calculations, short-circuit and arc-flash analysis, construction-ready drawings, Revit and Inventor modeling, vendor coordination, and development of design standards. The engineer will collaborate with clients, mechanical and software teams, manufacturers, and production personnel while ensuring compliance with applicable codes, safety requirements, performance goals, and manufacturability.

Lead Orbital Industries’ communications strategy, brand voice, messaging, content, social presence, website copy, and press function. Translate complex AI, materials science, and hardware engineering work into credible narratives for investors, technical buyers, talent, media, and the broader market. Build category authority for AI Industrial and CurieOS while partnering with founders, technical teams, GTM, design, and external creative and press resources.

Lead and build Orbital’s Forward Deployed Engineering function. Deliver customer deployments hands-on through production coding, architecture, integrations, debugging, and operational optimization across scientific and industrial systems. Own engagements from discovery through adoption, establish technical standards and delivery practices, measure outcomes, and develop reusable tooling and playbooks. Hire, mentor, and manage engineers while serving as the escalation point for complex technical decisions and production issues.

Deliver CurieOS deployments into customer environments by writing production code, designing architectures, building integrations, and debugging complex systems. Embed with scientists and engineers to translate ambiguous industrial R&D problems into scalable solutions, define evaluation benchmarks, manage implementations through adoption, track engagement KPIs, and codify reusable integration patterns. The role requires adapting quickly across scientific and engineering domains while influencing the platform roadmap.

Operate, troubleshoot, and commission tray- and rack-level direct-to-chip test rigs. Plan and execute test campaigns, validate and clean test data for analysis, install and verify sensors and instrumentation, support rig builds and procurement, and report findings and anomalies to engineering teams to drive system efficiency.

The Machine Learning Engineer will develop AI systems for optimizing physical technologies, ensure high engineering standards, and push ML research boundaries while collaborating with domain experts.

Design, build, and operate core backend systems powering the Curie platform (APIs, data pipelines, graph databases, integrations with AI models). Own feature lifecycle from design through deployment and monitoring, improve observability and reliability, write well-tested maintainable code, collaborate with ML researchers and domain experts, and mentor peers.