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About This Role
About AutoFi
AutoFi is the leading provider of digital commerce technology that powers the sales and finance experiences for the most innovative brands and dealers in automotive. The AutoFi platform enables a more transactional buying experience with $4B in funded loans processed through AutoFi annually. AutoFi’s dynamic selling platform empowers dealers to sell vehicles more efficiently and profitably, both online and in the showroom. We are funded for years of future growth and backed by investors including Crosslink Capital, Santander Holdings USA, SVB Financial Group, Ford, BMW iVentures and JP Morgan Chase.
Our team is diverse \- spread out across the U.S. and Canada, we have backgrounds from finance and technology as well as deep experience in all areas of the auto space. We’re empathetic, gritty, curious, and humble owners of this business and are supported by some of the biggest names in the auto and financial industries as commercial partners. We’ve never been more excited about the opportunity in front of us to help transition the auto industry from offline to online. If changing a trillion\-dollar industry sounds exciting, we’d love to hear from you.
For more information, visit www.autofi.com. About the Role
The Operational Enablement Specialist is expected to play a pivotal role in designing, provisioning and maintaining both foundational and advanced learning experiences that enable both human teams and AI\-driven systems. This role will report into the VP Operations, and partner closely with personnel across the business to own the full lifecycle of enablement content including new hire onboarding, revenue generating activities, Product/Service offerings, operational and technical support activities. You will be responsible for information gathering and analysis through design, development, delivery and continuous improvement across a wide range of topics. This will include employee engagement in growth strategies and behaviors, internal systems and processes, as well as successful Product configuration to meet Dealership needs across varying business models and their requirements. You will create high\-quality enablement materials optimized for both human learning and AI comprehension, ensuring consistent, accurate and scalable knowledge, inclusive of training an AI Agent to accurately interpret, reason through, and answer complex operational and industry specific questions.
Candidates must be self\-driven, have the ability to lead, motivate and drive results within cross departmental teams that they do not directly manage. They must have strong communication and presentation skills, be well organized, attentive to fine details, able to work under pressure and have comfort persevering under ambiguity. Each member of our team is challenged to contribute in a variety of capacities across the entire organization and show their individual strengths, from product to customer experience.
### Responsibilities
- Participate in building a transparent, cohesive and collaborative team environment with a culture of open communication and feedback.
- Locate sources, gather \& analyze relevant information, inclusive of deep information gathering with subject matter experts, applications/tools, processes, procedures/SOPs and work instructions etc, for the purpose of uncovering implicit knowledge, assumptions, edge cases in order to develop relevant, robust internal training content and AI Agent enablement materials.
- Utilize a hands\-on approach in the development of training content related to systems, tools and processes to obtain current state, real\-world information and examples, asking questions and digging deep to ensure validity. Ensure content supports complex question resolution, not just surface\-level explanations.
- Design and provision foundational new\-hire onboarding content as well as in\-depth, role\-specific learning experiences, working with the respective teams to design training solutions in relation to needs expressed, balancing adult learning styles to engage and use individual’s time effectively.
- Develop structured learning paths that progress from conceptual understanding to practical application. Translate complex growth, operational, technical and industry knowledge into clear, consumable learning modules.
- Curate content and training materials including but not limited to written guides, knowledge base articles, videos and quizzes both through our documentation repository as well as our Learning Management System (LMS), aligning elements of training to Process documentation as needed.
- Translate nuanced business logic, edge cases and conditional workflows into formats optimised for AI understanding and retrieval. Curate and refine AI training content to improve reasoning, consistency and contextual awareness.
- Ensure content remains accurate, relevant and aligned with evolving business needs over time. Maintain and regularly update materials to reflect product updates and system changes, ensuring both human teams and the AI Agent are effectively trained on changing standards.
- Evaluate and report on training program effectiveness through knowledge assessments, surveys, feedback etc, leveraging findings to refine and enhance these programs, their related materials and improve training effectiveness.
- Participate in change mgmt activities associated with implementing solutions that require training including coordination between teams, presentations/training, and similar.
- Source external training content and courses as appropriate to assist with individual skills development across the Operations org.
- Participation in the development of data tracking and KPIs to measure the progress and impact of large training initiatives and their evolution over time.
### Qualifications
- 3\+ years proven work experience within the Adult Learning, Training \& Development discipline.
- Strong experience building and launching training initiatives within RevOps/Sales, Operations and/or similar.
- Strong experience designing learning content from scratch and iterating on existing materials.
- Experience breaking down complex, ambiguous topics to create clear, structured documentation/training that supports advanced problem solving and decision making.
- Experience working with AI Agents, chatbots, or knowledge\-driven automation systems, familiarity with prompt design, knowledge structure and AI training methodologies.
- Ability to work well with technical and non\-technical team members at various levels, ranging from support to development.
- Experience cultivating trust and building relationships interdepartmentally with cross\-functional SMEs.
- Excellent written/verbal communication and presentation skills.
- Excellent organization, analytical, problem solving and critical thinking skills.
- Familiarity with Jira, Postman, Salesforce and LMS systems considered an asset.
- Automotive industry experience and/or familiarity with Dealership business models is a big plus!
$73,000 \- $85,000 a year
Annual Performance bonus \- $2,000
What's in it for you:* We offer full training and a competitive total rewards package along with great benefits
\- Medical, Dental \& Vision coverage \- 100% premium coverage for employee / 50\+% for dependents
- Flexible work hours
- Remote environment
- Competitive pay
- Visionary leadership team
- Growth opportunities within a dynamic culture
- Wellness \& cultural initiatives (fitness challenges, wellness webinars, virtual games, regional activities, etc.)
- Up to $1K per year for employee professional development
\- Stock options \- we are all owners!
Individual compensation decisions are based on a number of factors, including the candidate’s experience and qualifications and local market conditions. Please note, the foregoing salary range does not reflect an employee’s total compensation package, which may include bonus, company equity, and health benefits.
AutoFi is an equal opportunity employer. Individuals seeking employment are considered without regards to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, sexual orientation, gender identity or other protected status under all applicable laws, regulations, and ordinances.
Personal Information submitted as part of your application is subject to our website privacy policy, located at https://www.autofi.com/privacy\-policy/
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $73K-$85K range is below the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Autofi, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($79K) sits 53% below the category median. Disclosed range: $73K to $85K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Autofi AI Hiring
Autofi has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $85K - $85K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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