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About This Role
At Podium, we bring AI Employees to local businesses that turn every conversation into revenue. Trusted by 60,000\+ businesses across Auto, Home Services, and Aesthetics, Podium captures and converts leads 24/7, driving both new business and repeat customers.
In under 24 months, we crossed $100M in AI Agent ARR, scaling 300% year\-over\-year. During this time, we’ve deployed 10,000 AI employees to empower real business outcomes for our customers. Podium is building what we believe will be the most impactful AI employee ecosystem for local business.
Podium has been recognized as the Best AI Implementation by Inc. Magazine, highlighted by OpenAI for building revenue\-driving AI Agents, and awarded the \#1 AI Agent for Business Operations by G2\.
Our growth is fueled by hiring exceptional people, holding them to high standards, and creating opportunities for them to grow and make an impact. Our operating principles guide daily behavior and ensure we hire people who will thrive at Podium. If you're hungry for growth, aligned to our operating principles, and ready to get to work, you won't find a better place to learn and accelerate your career.
At Podium, our mission is to arm every local business with a complete platform and outcome\-driven AI employees that convert leads into real, paying customers. Every day, millions of workers use our AI lead conversion and communication platform to help them get more leads and make more money.
As a Field AI Launch Manager , you will lead high\-impact onboardings for large dealership and OEM groups, helping deploy Podium’s AI solutions and operating system across Sales, Service, and Voice. This is a hybrid role with 50–75% onsite travel , combining customer\-facing consulting, in\-dealership training, and technical backend setup. You will work closely with dealership teams to understand their workflows, configure the right solutions, and drive adoption in the real world.
This role requires someone who is deeply curious about AI, technically minded, comfortable with ambiguity, and willing to show up with grit. You should understand how dealerships operate, be able to learn quickly, and bring both strategic thinking and hands\-on execution to every launch.
What you will be doing:
Lead onsite implementations and onboarding for large OEM groups and dealership networks
Deploy Podium AI solutions and operating system capabilities across Sales, Service, and Voice
Train dealership teams in person and virtually to drive adoption and workflow change
Own technical backend setup, configuration, and coordination with internal teams
Partner with customers to understand business processes and translate them into effective product deployments
Act as both a consultant and subject matter expert throughout the launch process
Navigate ambiguity, solve problems quickly, and keep complex implementations moving forward
Build trusted relationships with dealership stakeholders and executive sponsors
Surface product feedback and implementation learnings back to internal teams
Travel onsite 50–75% of the time, depending on customer need
What you should have:
5\+ years of experience in implementation, consulting, customer success, technical onboarding, or related roles
Experience working with dealerships, automotive groups, OEMs, or similar complex customer environments
Strong technical aptitude and the ability to learn new systems quickly
A genuine curiosity about AI and how it can improve customer workflows and business outcomes
Comfort operating in ambiguity and solving problems without a perfect playbook
Strong communication and training skills, including the ability to influence both frontline users and leadership
A consultative mindset with the ability to balance strategy, execution, and customer trust
Grit, ownership, and a bias toward action
Benefits
Open and transparent culture
Life insurance, long and short\-term disability coverage
Paid maternity and paternity leave
Fertility Benefits
Generous vacation time, plus three 4\-day summer holiday weekends
Excellent medical, dental, and vision benefits
401k Plan
Bi\-annual swag drops with cool Podium gear and apparel
Podium is an equal opportunity employer. Podium provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, gender, national origin, sexual orientation, gender identity or expression, age, disability, genetic information, marital status or veteran status.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Podium, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Podium AI Hiring
Podium 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 $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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