Interested in this AI/ML Engineer role at Slate Auto?
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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 the VP – Distinguished Engineer of Generative AI Engineering at Slate Automotive, you will be the founding architect of our GenAI capability. We are not just building an electric truck — we are building an AI-native company. You will design, build, and ship the Generative AI systems that transform how we engineer vehicles, run manufacturing, and serve customers. This is a deeply hands-on role: you will write code, make architectural calls, and lead by example.
You will build and lead a lean, high-leverage team of GenAI engineers, MLOps engineers, and applied scientists — and personally stay in the code — to solve challenges no one in the auto industry has cracked yet.
WHAT YOU WILL DO
- Build the GenAI Stack from Scratch: Architect and ship the end-to-end GenAI platform — context layer, data layer, model serving, agent frameworks, and evaluation pipelines — on a modern cloud foundation.
- Lead by Building: You are a player-coach. You will recruit and grow a world-class team of GenAI engineers, but you will stay in the code — reviewing PRs, making architecture calls, and shipping alongside your team.
- Ship Agentic Systems Across the Company: Deploy GenAI agents across vehicle engineering, manufacturing, supply chain, software development, and GTM — converting human-driven workflows into AI-driven ones that run faster and cheaper.
- Build Slate’s Proprietary AI Moat: Construct the Context Layer — a living knowledge base of agentic, human, robotic, and enterprise decisions — and the unified Data Layer that trains purpose-built models on real Slate decisions. This IP is our long-term competitive advantage.
- Drive GenAI into the Physical World: Take GenAI beyond the laptop — partner with Vehicle Engineering, Manufacturing, and Quality teams to embed AI into the factory floor, design tooling, and supply chain, upgrading traditional auto manufacturing from the inside.
- Set the Standard for AI-First Engineering: Establish the engineering culture, standards, and practices that make Slate a destination for world-class AI talent — and that make our GenAI output measurably better than the industry.
WHAT YOU BRING
- 12+ years of engineering experience, including 3+ years building and shipping production GenAI systems (LLM-based applications, agents, RAG pipelines), with 5+ years in a senior technical leadership capacity.
- GenAI Architecture: Deep hands-on experience with LLM APIs (OpenAI, Anthropic, Gemini), open-source model deployment, vector databases, agent orchestration frameworks (LangChain, LlamaIndex, or similar), and prompt engineering at scale.
- MLOps & Production Systems: Proven track record taking GenAI from prototype to production — including evaluation frameworks, guardrails, observability, and continuous improvement loops.
- Fine-Tuning & Custom Models: Experience fine-tuning and training purpose-built models for domain-specific tasks. Familiarity with the tradeoffs between hosted APIs, open-source models, and custom training for cost, latency, and IP reasons.
- Data & Cloud Foundation: Solid grounding in modern data stacks (Snowflake, Databricks, dbt) and cloud infrastructure (AWS/GCP/Azure). Comfortable reasoning about cost, latency, and data architecture as first-class constraints.
- Academic Grounding: BS required; MS or PhD in Computer Science, Machine Learning, or a related field preferred — but exceptional track record beats pedigree every time.
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.
$222,431.00 - $370,719.00 Base Pay Range (Annual)
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: $178K across 2239 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 11,520 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 $212,040 based on 13,780 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 $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Slate Auto AI Hiring
Slate Auto has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Troy, MI, US, Troy, MI, WA, US. Compensation range: $260K - $370K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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