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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 WE ARE LOOKING FOR
The Finance Analytics \& AI Engineer is a senior individual contributor role based on the Commercial FP\&A team, with scope to support the broader Finance organization. This role is the foundational technical hire responsible for architecting and building the reporting analytics and AI\-driven automation that powers commercial decision making.
A defining feature of this role is helping carry Finance reporting and analytics through Slate’s planned ERP migration from NetSuite to SAP (targeted for early 2027\), ensuring data models, pipelines, and reporting remain stable and accurate across the transition. In the near term, NetSuite remains the system of record while SAP is stood up, so you should be comfortable building on NetSuite today and designing for portability to SAP.
WHAT YOU GET TO DO
Own the Finance Data \& Intelligence Layer
- Architect and scale Slate’s Finance reporting and analytics ecosystem across all Finance functions — including Commercial and Corporate FP\&A, Accounting/Controllership, Treasury, and Procurement — as well as partner organizations such as Product Development
- Design and maintain robust data models that integrate the ERP (NetSuite today, migrating to SAP by early 2027\), EPM (Adaptive Planning), and operational systems
- Build scalable data pipelines and layers to enable self\-service analytics through AI agents
Lead Finance AI \& Automation Strategy
- Define and execute Slate’s Finance AI roadmap, including:
+ AI\-powered variance analysis (PVM, BvA/FvA)
+ Automated forecast updates and anomaly detection
+ Inventory optimization
+ Natural language query tools for business users
- Build and deploy AI agents that:
+ Connect to finance and operational datasets
+ Enable leaders to query performance (e.g., SKU\-level GM trends, pricing impacts)
+ Automate recurring Finance workflows
Build Best\-in\-Class Dashboards \& Reporting
- Develop executive\-ready dashboards and reporting across:
+ Gross margin by product, channel and unit economics
+ Opex \& Capex forecasting \& actuals reporting
+ Sales, inventory, and take rate analyses
+ Plant KPI reporting (manufacturing cost, throughput, and operational performance)
+ Business performance dashboard consolidating company\-wide financial and operational KPIs for leadership
+ Sales KPI tracking (bookings, deliveries, and channel performance)
- Partner Finance and Commercial stakeholders to standardize KPIs and reporting definitions
- Deliver real\-time insights for leadership (including Board\-level materials)
Drive Advanced Analytics \& Decision Support
- Develop models for:
- Pricing optimization and margin expansion
- Demand forecasting and inventory planning
- Scenario modeling and long\-range planning
Enable a Self\-Service Data Culture
- Build tools that allow non\-finance stakeholders to access and interpret financial data
- Implement data governance and output validation into AI models
- Train business partners on dashboards, tools, and AI capabilities
- Reduce manual reporting and elevate the organization toward real\-time, insight\-driven decision making
WHAT YOU BRING
We are seeking a highly technical, product\-minded senior individual contributor who can bridge finance, data engineering, and AI. This person will be foundational in building Slate’s next\-generation Finance function.
- 8\+ years in FP\&A, Business Intelligence, Data Analytics, or related fields — or equivalent demonstrated capability across FP\&A, data engineering, and AI
- Strong finance acumen (P\&L, gross margin, unit economics, forecasting)
- Advanced expertise in BI tools (Tableau, Power BI, Looker, etc.)
- Experience working with ERP/EPM systems (NetSuite, SAP, and Adaptive Planning preferred)
- Strong SQL and data modeling capabilities
- Experience building data pipelines and working with data warehouses/lakes
- Familiarity with Python or similar for analytics and automation
- Experience integrating multiple data sources into a unified reporting layer
AI \& Automation Experience (Required)
- Demonstrated hands\-on experience building and deploying AI/ML models, agents, and analytics workflows in production (not experimental or coursework only)
- Working proficiency with LLMs and agent\-based architectures
- Experience using AI tools to automate reporting, forecasting, or analysis
- Strong interest in building AI\-driven finance capabilities from the ground up
Mindset
- Builder mentality: thrives in ambiguous, fast\-moving environments
- Highly analytical with strong attention to detail
- Strong communicator who can translate data into business insights
- Operates with urgency and a bias toward action
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
slate\-talent\[email protected].
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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Slate Auto AI Hiring
Slate Auto has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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