VP-Distinguished Engineer of Generative AI Engineering

Re:Car•Troy, MI

12d

About The Position

As the VP – Distinguished Engineer of Generative AI Engineering at Slate Automotive, you will be the founding architect of the company’s Generative AI capability. You will design, build, and ship systems that transform vehicle engineering, manufacturing, and customer service. This is a hands-on player-coach role that combines coding and architectural leadership with building and leading a lean team of GenAI engineers, MLOps engineers, and applied scientists. You will build Slate’s GenAI platform and deploy agentic systems across the company. You will also develop proprietary context and data layers based on Slate’s decisions, and work with vehicle engineering, manufacturing, and quality teams to bring AI into factory operations, design tooling, and the supply chain. The role will establish engineering standards and practices for AI-first development. Slate describes itself as an AI-native automotive startup building safe, reliable, customizable vehicles in the USA. The position offers an annual base pay range of $222,431 to $370,719. Benefits include medical, dental, vision, life and disability insurance, vacation, and a 401(k). The successful candidate may also be eligible for equity and/or a discretionary annual incentive program, subject to program rules.

Requirements

At least 12 years of engineering experience.
At least 3 years building and shipping production GenAI systems, including LLM-based applications, agents, or RAG pipelines.
At least 5 years in a senior technical leadership role.
Hands-on experience with LLM APIs such as OpenAI, Anthropic, or Gemini; deploying open-source models; vector databases; agent orchestration frameworks such as LangChain or LlamaIndex; and prompt engineering at scale.
A track record of taking GenAI systems from prototype to production, including evaluation frameworks, guardrails, observability, and continuous-improvement loops.
Experience fine-tuning and training purpose-built models for domain-specific tasks.
Ability to assess tradeoffs among hosted APIs, open-source models, and custom training in relation to cost, latency, and intellectual property.
Grounding in modern data stacks such as Snowflake, Databricks, and dbt, and cloud infrastructure such as AWS, GCP, or Azure.
Ability to reason about cost, latency, and data architecture as key constraints.
Bachelor of Science degree required.

Nice To Haves

Master’s or PhD in Computer Science, Machine Learning, or a related field.
Exceptional professional track record; the posting notes that this can outweigh academic pedigree.

Responsibilities

Architect and ship an end-to-end GenAI platform, including its context and data layers, model serving, agent frameworks, and evaluation pipelines, on a modern cloud foundation.
Recruit and grow a team of GenAI engineers while contributing hands-on through coding, pull-request reviews, architectural decisions, and shipping work alongside the team.
Deploy GenAI agents across vehicle engineering, manufacturing, supply chain, software development, and go-to-market workflows.
Build a proprietary context layer that records agentic, human, robotic, and enterprise decisions, as well as a unified data layer for training models on Slate-specific decisions.
Partner with Vehicle Engineering, Manufacturing, and Quality teams to integrate AI into factory operations, design tooling, and the supply chain.
Establish engineering culture, standards, and practices that attract AI talent and improve the quality of GenAI output.