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About This Role
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. 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.
WHAT YOU BRING
- 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.
- Candidates with this background will move faster in the role; those without it should expect to develop it on the job. 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.
SALARY RANGE
The compensation for this position is the range Slate reasonably and in good faith expects to pay for the position taking into account the wide variety of factors that are considered in making compensation decisions, including job-related knowledge; skillset; experience, education and training; certifications; work location; and other relevant business and organizational factors.
Total Base Pay Range- $222,431.00 - $370,719.00
Additional Compensation and Benefits: Slate offers a wide range of competitive benefits, including medical, dental, vision, life insurance, disability insurance, vacation, and 401k. The successful candidate may also be eligible to participate in the equity program and/or a discretionary annual incentive program, subject to the rules governing such programs.
WHY JOIN TEAM SLATE?
At Slate, we’re fueled by grit, determination, and attention to detail. The start-up spirit of ingenuity and resourcefulness move our business forward. Team Slate fosters a culture of excellence, innovation, and mutual respect, and is motivated by shared principles.
- Safety First
- Delight Customers
- One Team
- Relentless Improvement
- Fast, Frugal, and Scrappy
- Respectful Collaboration
- Positive Legacy
WE WANT TO WORK WITH PEOPLE THAT REFLECT THE COMMUNITIES IN WHICH WE OPERATE.
Slate is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, veteran status, marital status, parental status, cultural background, organizational level, work styles, tenure and life experiences. Or for any other reason.
Slate is committed to providing reasonable accommodation for qualified individuals with disabilities in our job application procedures. If you need assistance or an accommodation due to a disability, you may contact us at
Salary Context
This $222K-$370K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Slate Auto, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. This role's midpoint ($296K) sits 40% above the category median. Disclosed range: $222K to $370K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Slate Auto AI Hiring
Slate Auto has 4 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span CA, US, NV, US, Remote, US. Compensation range: $185K - $370K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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