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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.
Position Overview
Slate Automotive is building an AI-native vehicle platform from the ground up. This role is for an engineer ready to work on real AI/ML problems in production: GenAI features, data pipelines, and AI applied to manufacturing and supply chain operations.
We are open to candidates early in their careers if they have the right foundation. A PhD in a relevant field is a strong signal; engineers without one should bring equivalent depth through demonstrated project work, research, or professional experience. What matters is that you can build things, learn fast, and are genuinely interested in applying AI to physical and operational systems.
The role reports to the Distinguished Engineer of Generative AI.
What You Will D o
Build and ship AI/ML features. Work across the model lifecycle: data preparation, training or fine-tuning, evaluation, deployment, and monitoring. Own features end-to-end and iterate based on real production feedback.
Contribute to GenAI systems. Build RAG pipelines, work with LLM APIs and open-source models, design prompts for reliability, and contribute to agentic workflows. You will work on the full GenAI stack hands-on.
Build data and evaluation infrastructure. Write data pipelines, labeling workflows, and evaluation frameworks. Reliable evals are a first-class deliverable on this team.
Work on manufacturing, supply chain, and physical operations problems. Slate operates a factory with a real supply chain. You will be exposed to AI problems in both areas: computer vision for quality inspection, predictive maintenance, and sensor data on the physical side; demand forecasting, inventory planning, supplier risk, and logistics on the supply chain side. We are particularly interested in candidates who are drawn to this kind of work.
Collaborate across the organization. Work with Vehicle Engineering, Manufacturing, and Operations to understand requirements and translate them into AI systems that produce measurable output.
Who You Are
Technically grounded in ML. You understand how models are trained and evaluated, not just how to call an API. You have built something end-to-end — a project, a thesis, a production system — that demonstrates that.
Interested in physical and operational AI. Candidates drawn to the intersection of AI and the physical world — manufacturing systems, robotics, logistics, industrial data — will find the most to work on and will ramp fastest. Not a requirement, but a clear differentiator.
A fast learner. The GenAI landscape moves quickly and so does Slate. You pick up new tools and domains without needing everything handed to you.
Hands-on. You write code, run experiments, and ship things. Research interest without engineering follow-through is not a fit for this role.
Collaborative and clear. You work well across disciplines and can explain technical decisions to non-technical stakeholders. Be willing to directly interreact with stakeholders to build product without the need for a product manager.
Technical Requirements
A PhD in a relevant field is a strong foundation for someone early in their career and is treated as such. Candidates without a PhD should bring 3+ years of professional or research experience working directly on ML systems. In either case, the bar is the same: you need to demonstrate you can build.
Machine Learning and Generative AI
- Foundational understanding of ML: model training, loss functions, evaluation metrics, overfitting, and regularization.
- Practical experience with: supervised learning, NLP, computer vision, and time-series modeling.
- Familiarity with LLM APIs (OpenAI, Anthropic, Gemini, or similar) and how to build reliably on top of them.
- Basic exposure to RAG, embeddings, or retrieval systems — including in a project or research context.
Ability to evaluate model quality rigorously, not just report accuracy on a held-out set. *
Software Engineering
- Python proficiency: comfortable with the ML stack (PyTorch or JAX, Hugging Face, pandas, scikit-learn).
- Ability to write production-quality code, not just notebook code.
- Familiarity with cloud platforms (AWS, GCP, or Azure) at a working level.
Version control, experiment tracking, and basic MLOps practices. *
Manufacturing, Physical AI, and Supply Chain (Valued)
Preferred. Candidates with background or genuine interest in any of the following will stand out.
Academic or professional background in Mechanical Engineering, Electrical Engineering, Robotics, Industrial Engineering, or a related physical discipline.
Exposure to computer vision, sensor data, or real-time systems — including coursework or personal projects.
Familiarity with supply chain, logistics, or operations research problems.
Experience with simulation environments or physical hardware in a research or lab setting.
Academic Grounding
BS required. MS or PhD in Computer Science, Machine Learning, Robotics, Electrical Engineering, Mechanical Engineering, Industrial Engineering, or a related field preferred. A PhD is treated as a strong signal for early-career candidates. Candidates without advanced degrees should bring equivalent depth through their project or professional work.
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.
123,339.00 - 154,174.00 - 185,009.00 USD Annual Base
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 $123K-$185K range is below the median 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($154K) sits 27% below the category median. Disclosed range: $123K to $185K.
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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