VP – Distinguished Engineer of Generative AI Engineering

Slate Auto

7d•$222,431 - $370,719

About The Position

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.

Requirements

15+ years of engineering experience, including 5+ years shipping production GenAI systems and 7+ years in a senior technical leadership role. Candidates are expected to have work that is recognized outside their own organization.
BS required. MS or PhD in Computer Science, Machine Learning, Robotics, Electrical Engineering, Mechanical Engineering or a related field preferred. Exceptional track record beats pedigree. Candidates without advanced degrees who have published, built widely-adopted systems, or otherwise demonstrated research-level thinking through their work will be evaluated accordingly.
Deep hands-on experience with LLM APIs (OpenAI, Anthropic, Gemini) and open-source model deployment at production scale.
Vector databases, agent orchestration frameworks (LangChain, LlamaIndex, or equivalent), and prompt engineering for reliability, not just capability.
Fine-tuning and training purpose-built models for domain-specific tasks, including the tradeoff analysis between hosted APIs, open-source models, and custom training across cost, latency, and IP dimensions.
Evaluation frameworks, guardrails, and observability pipelines for GenAI in production: the ability to know whether a system is working, not just whether it is running.
Experience designing and deploying multi-agent systems in production, including coordination protocols, failure isolation, and human-in-the-loop escalation paths.
Deploying AI systems that operate on physical hardware: robots, CNC machines, quality inspection systems, assembly line sensors, or equivalent industrial environments.
Computer vision systems for manufacturing quality assurance: defect detection, dimensional inspection, or process monitoring at production-line throughput.
Closed-loop learning systems where model behavior is updated based on physical world outcomes, including approaches to safe online learning in environments where errors have physical consequences.
Robot-to-model feedback architectures: designing systems where robotic process data improves model performance and model outputs improve robotic process control.
Familiarity with physical simulation environments (Isaac Sim, MuJoCo, or similar) for model development and validation before factory deployment.
Sensor fusion and multimodal data handling for environments with structured sensor data, unstructured imaging, and natural language interfaces operating simultaneously.
Designing real-time inference pipelines with hard latency constraints and failure consequences, including edge deployment architectures that operate under intermittent connectivity.
Data architecture for AI at scale: modern data stacks (Snowflake, Databricks, dbt), vector stores, and streaming data pipelines that handle both batch and real-time model consumption.
Cloud infrastructure across AWS, GCP, or Azure at the architecture level, including cost modeling, capacity planning, and the tradeoffs between managed AI services and self-hosted infrastructure.
Designing and owning AI platforms that serve enterprise-scale internal usage: thousands of concurrent users, dozens of integrated systems, and SLA requirements that real operations depend on.
Multi-tenant model serving architectures with cost isolation, quota management, and workload prioritization across competing internal consumers.
Cloud infrastructure across AWS, GCP, or Azure at the architecture level, including cost modeling, capacity planning, and the tradeoffs between managed AI services and self-hosted infrastructure.
Security and compliance architecture for AI systems handling proprietary manufacturing IP, including data residency, access controls, and audit trails.
Platform engineering practices that make GenAI accessible and reliable for internal teams who are not AI practitioners: SDKs, abstraction layers, and self-service tooling.

Nice To Haves

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. Strong opinions about what works in production, grounded in having shipped it.
Technically credible at the architecture level. You can translate a product requirement into a concrete system design with defensible tradeoffs, and operate effectively at the frontier of what is currently buildable.
Comfortable in a resource-constrained environment. You build systems that perform above their weight, make pragmatic calls under uncertainty, and move fast without accumulating architectural debt that stalls the team later.
Candidates with this background will move faster in the role; those without it should expect to develop it on the job.

Responsibilities

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.