VP – Distinguished Engineer of Generative AI Engineering

ABOUT SLATE

At Slate, we’re building safe, reliable vehicles that people can afford, personalize and love—and doing it here in the USA as part of our commitment to reindustrialization. The spirit of DIY and customization runs throughout every element of a Slate, because people should have control over how their trucks look, feel, and represent them.

WHO ARE WE LOOKING FOR

As Distinguished Engineer of Generative AI, you are the founding technical authority for AI at Slate . You will help envision, design and ship the GenAI platform for Slate.Auto , write code, and make architectural decisions that carry long-term consequence.

The scope covers GenAI platforms, agentic systems, robot-to-model feedback loops, and embedded AI across the factory floor. You will partner with Vehicle Engineering, Manufacturing, and Quality to deploy AI where it produces measurable output.

The role reports directly to the Chief Digital and Operations Officer and carries a seat on the senior technology leadership team.

WHAT YOU WILL DO

• Design and own the end-to-end GenAI platform: context layer, data layer, model serving, agent frameworks, and evaluation pipelines. Architecture decisions here must hold at enterprise scale, across multiple product lines, manufacturing sites, and a growing engineering organization. This means designing for reliability, cost efficiency, and extensibility from day one, not retrofitting those properties after the fact.
• Build Industry-Leading AI Systems. Design systems that practitioners at frontier AI labs would recognize as technically sound, applied to physical manufacturing: novel approaches to context management, agentic coordination across physical and digital systems, and model personalization on proprietary manufacturing data.
• Drive Physical AI Across the Vehicle Program. Take AI beyond the laptop. Slate’s vehicles are built in the real world, by real robots, on a factory floor that generates sensor data, failure modes, and edge cases that no benchmark captures. You will embed AI directly into manufacturing: robotic process control, computer vision for quality assurance, predictive maintenance systems, and closed-loop feedback between physical production and model behavior. The goal is to dramatically improve to design and build a new vehicle.
• Build Slate’s Proprietary Data and Context Layer. Construct a knowledge base of agentic, human, robotic, and enterprise decisions that compounds over time. Build the unified Data Layer that trains purpose-built models on real Slate decisions across the vehicle lifecycle. You are responsible for the architecture of both.
• Ship Agentic Systems Across the Company. Deploy production-grade AI agents across vehicle engineering, manufacturing, supply chain, software development, and GTM. These are not prototypes or proof-of-concepts: they are systems that run workflows that previously required humans, at a quality level that earns trust. You will establish evaluation frameworks, guardrail standards, and observability practices that make these systems auditable.
• Lead by Building. Recruit and grow a lean team of GenAI engineers, MLOps engineers, and applied scientists. Stay in the code: review PRs, make architecture calls, and ship alongside the team. Your technical judgment sets the quality bar.
• Set the Engineering Standard. Establish the technical standards, practices, and hiring bar for the GenAI organization. Contribute externally where appropriate: open-source, publications, or conference talks.
• Hands-on. You have built GenAI systems from scratch in production and you still write code. You can point to specific systems you personally designed that are running at scale today. You are not afraid to directly interact with stakeholders to understand and develop requirements.
• An architect at scale. You have designed enterprise-level platforms that serve thousands of internal users, integrate with dozens of upstream and downstream systems, and hold up under operational stress. You know what breaks at scale before it breaks, because you have seen it break before.
• A Physical AI practitioner (preferred). You have applied AI to physical systems: robotics, manufacturing, IoT, autonomous vehicles, or another domain where data is messy, latency constraints are real, and failure modes have physical consequences.
• GenAI native. Deep hands-on experience with LLMs, agent frameworks, RAG, and fine-tuning. Stro…