Senior/Principal Lead Delivery Engineer — Industrial

YOUR IMPACT

As the Senior/Principal Lead Delivery Engineer for Industrial, you own customer outcomes for Luminary's engagements with heavy machinery, industrial equipment, HVAC, pumps, turbomachinery, and engineered products companies. You are a dual hat — project manager and product manager — leading a matrixed value delivery team that converts customer engineering challenges into Physics AI solutions across structural, thermal, electromagnetic, and multi-physics workloads. You partner with the GTM team on account engagement and growth.

WHAT YOU'LL DO

• Own end-to-end customer outcomes across new and existing industrial accounts — from discovery and use case scoping through model development, deployment, and embedding into customer engineering workflows.
• Author Statements of Work (SOWs), educate customers on the engagement plan, and own delivery accountability against them.
• Lead a value delivery team in a matrix structure: Applications Engineers, Applied AI/ML Scientists, Data and Platform Engineers, and selected product engineers. Set technical direction, sequence the work, remove blockers.
• Operate as a dual hat — project manager and product manager — running sprints, milestones, and risk while also choosing the right use cases and shaping the product intuition for what to build next.
• Stay hands-on-keyboard where it matters. Debug a model, restructure a data pipeline, prototype a workflow integration. Lead from the front, not from slides.
• Embrace co-engineering: work side-by-side with industrial engineers on use case development, sharing knowledge and building trust through visible, collaborative work.
• Partner with the GTM tream on new account engagement, GTM-to-delivery handoff, and account growth.
• Bring market and customer signal back to Product and Research, directly shaping Luminary's roadmap for industrial use cases.

WHAT YOU BRING

• 5–10 years of experience in a technical role in a relevant industry (engineering, simulation, R&D, advanced engineering, technical program management, or applied AI/ML). Principal-level candidates have an extensive track record in the industry.
• Multi-physics fluency across industrial engineering disciplines: structural mechanics, thermal analysis, electromagnetics, rotating machinery, fluid dynamics, or multi-physics optimization. Generalist depth across several disciplines is more valuable than world-class depth in one.
• Strong hands-on engineering chops — Python, scripting, simulation tools, and AI/ML workflows. Comfortable diving into code, models, or simulation setups when needed.
• Working knowledge of AI/ML applied to engineering — surrogate modeling, neural operators, physics-informed ML, or digital twins.
• Demonstrated ability to lead cross-functional technical teams without direct authority. You influence through clarity, credibility, and outcomes.
• Product instinct: a track record of choosing the right problems to solve and shipping things that get used — not just demos that get cited.
• Customer-facing presence: equally credible explaining a physics model to a chief engineer and walking a customer roadmap with Luminary leadership.
• Self-starter mentality, comfortable with ambiguity, persistent through iteration. You question what you are asked to do and prioritize what actually moves the needle.
• Willing to travel 10–25% to customer sites.

PREFERRED QUALIFICATIONS

• Advanced degree (MS or PhD) in Mechanical, Industrial, Manufacturing, Electrical, or Materials Engineering.
• Prior experience at major industrial companies (e.g., Siemens, GE, Carrier, Trane, Grundfos, Flowserve, Caterpillar, Atlas Copco, Cummins) or enterprise software companies serving industrial sectors.
• Experience with engineering challenges specific to rotating machinery, heat exchangers, pumps and compressors, or similar complex engineered products.
• Track record of driving technology adoption in industrial environments with measurable business impact (reduced development time, improved performance, lower prototyping costs).
• Experience deploying ML or simulation tooling into production engineering workflows.