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About This Role
About Pearl
Pearl is the global leader in dental AI. Our FDA\-cleared computer vision platform helps dental practices detect disease more accurately, treatment plan more effectively, and build patient trust through transparent AI\-assisted diagnostics. We’re scaling rapidly, and serve thousands of dental practices ranging from solo practitioners to the largest DSOs in North America. Our products include Second Opinion (real\-time radiograph analysis), Practice Intelligence (clinical analytics), and PreCheck (pre\-visit patient engagement).
Who We’re Looking For
We're hiring a Director of AI Operations (Go\-to\-Market) to serve as the right hand of the Chief Operating Officer and a key driver of Pearl's commercial acceleration. You will sit inside the revenue organization with a direct mandate to find and unlock growth, across private practices and enterprise DSO customers, by building the operational infrastructure that makes our go\-to\-market engine faster, smarter, and more scalable.
This is not a strategy or analytics role. It's a builder role for an execution\-obsessed, revenue\-first operator who thrives on owning outcomes. You will ensure that Sales, Customer Success, and Marketing have the processes, tooling, workflows, and automation they need to perform at scale, and you'll be the one identifying where we're leaving growth on the table and fixing it. You will be accountable for unifying currently fragmented operational motions across all growth functions into a single, cohesive operating system.
You'll manage our outbound tooling stack (Clay, Nooks, and evolving products), and work closely with the data team to build the reporting and attribution infrastructure Sales and Marketing leadership rely on to make decisions. You'll also be handed a sandbox of AI tools and the latitude to reinvent how Marketing, Sales, and Customer Success operate, with the expectation that you use it.This role explicitly excludes the finance/deal desk component of sales operations (compensation, commissions, billing).
You won't just be handed tools, you'll be expected to stay at the frontier, experiment with emerging LLM and AI agent capabilities, and be the person on the revenue team who knows what's possible before anyone asks.
Key Responsibilities
*Funnel Growth \& Optimization: “the core of this role”*
- Proactively identify revenue leaks and conversion opportunities across the funnel, from lead quality and demo show rates to SDR\-to\-AE handoff loss, and own the operational fixes that move the number.
- Treat the funnel as a growth asset: continuously test, instrument, and optimize each stage to compound conversion rate improvements across SMB and DSO motions.
- Surface and prioritize the highest\-leverage interventions, sequencing, enrichment, routing, timing, and drive execution with Sales and Marketing rather than waiting for leadership to diagnose the problem.
- Build the measurement infrastructure that makes growth opportunities visible before they become misses: forecast gaps, segment underperformance, channel attribution anomalies.
- Partner with Sales leadership to find hidden capacity in the existing pipeline: stalled deals, under\-worked segments, and low\-touch accounts with expansion potential.
- Own end\-to\-end visibility into the revenue funnel, from lead creation through closed\-won, ensuring every stage is instrumented, defined, and trusted across SMB and DSO motions.
*Sales Operations: built to accelerate rep output and pipeline velocity*
- Partner with the Data team to drive accuracy in pipeline, activity, and sales forecasting data across SMB and DSO segments.
- Design and maintain lead routing, territory assignment, opportunity stage definitions, and pipeline hygiene standards.
- Build and manage the outbound tooling ecosystem (Clay for enrichment, Nooks for parallel dialing, and future tools) with a focus on rep productivity and data quality.
- Own SDR and AE workflow automation: sequences, task queues, lead scoring models, and handoff triggers between SDR AE CS.
- Partner with Sales leadership and the Outbound Sales Leader to build forecasting models, pipeline coverage reporting, and rep productivity dashboards.
- Drive sales process standardization and compliance — ensuring reps follow defined stages, capture required fields, and maintain data discipline.
*Marketing Operations: built to compound demand and improve funnel economics*
- Own the marketing technology stack and its integration with CRM: forms, landing pages, email automation, nurture workflows, lead scoring, and lifecycle stage management.
- Partner with Demand Generation to build and maintain marketing attribution models (first\-touch, multi\-touch, influenced pipeline) that connect marketing activity to pipeline and revenue.
- Manage website operational workflows: form routing, chatbot logic, content personalization triggers, and conversion tracking (excluding paid media execution).
- Own list management, segmentation, data hygiene, and compliance (CAN\-SPAM, opt\-out management) across all marketing channels.
- Partner with Demand Generation and Content teams to operationalize campaigns — building the backend workflows that turn strategy into execution.
*Customer Success Operations: built to protect and expand revenue*
- Partner with Customer Success to build the post\-sale operational infrastructure: onboarding workflows, activation tracking, health scoring, renewal/expansion triggers, and churn risk alerting.
- Own the CS tech stack integration layer — ensuring customer lifecycle data flows cleanly between HubSpot, product analytics, and support systems.
- Design NRR and gross retention reporting frameworks that CS leadership and the exec team rely on for strategic decisions.
- Operationalize the customer journey: define handoff points from Sales Onboarding CSM Renewal, with SLAs and escalation paths at each stage.
*Product\-Led Operations*
- Partner with the Product team to build self\-sign\-up and cross\-sell infrastructure that reduces friction in the acquisition motion and enables product\-led revenue.
- Connect the product stack (e.g., Pendo) to the broader marketing and customer success stack to create a unified view of customer behavior across the lifecycle.
- Design product\-led reporting frameworks that product leadership and the exec team rely on for strategic decisions.
*AI\-Powered Growth Operations*
- Lead Pearl's adoption of AI and LLM tools across the commercial stack — from AI\-assisted prospecting and outreach personalization to automated pipeline analysis and CS workflow intelligence.
- Continuously evaluate emerging tools (AI agents, LLM\-powered enrichment, conversational AI, autonomous workflow tools) and make build\-vs\-buy decisions that compound operational leverage.
- Build automated systems that reduce manual ops work across Sales, Marketing, and CS — using AI to scale output without scaling headcount linearly.
- Serve as the internal expert and evangelist on what AI can unlock in a go\-to\-market context, bringing POVs to leadership proactively rather than waiting to be asked.
*Team Leadership \& Cross\-Functional Partnership*
- Serve as the operational connective tissue between Sales, Marketing, CS, Product, and Finance — ensuring systems and processes are aligned and information flows without friction.
- Own vendor evaluation, selection, and management for growth tools and platforms.
- Drive operational planning cadences: QBRs, monthly operating reviews, territory planning, and capacity modeling in partnership with growth leadership.
Who You Are
- 7–10 years of progressive experience in revenue operations, sales operations, marketing operations, or growth operations at B2B SaaS companies.
- You've built and scaled ops infrastructure through at least one major growth phase (e.g., $10M $50M\+ ARR or $50M $150M\+).
- Deep, hands\-on HubSpot expertise — you're not just strategic, you can build complex workflows, custom objects, and reporting yourself.
- Experience with modern outbound tooling (Clay, Nooks, Apollo, Outreach, or similar) and a point of view on how to architect a high\-velocity outbound stack.
- You've managed ops teams and can hire, develop, and retain strong talent.
- Comfortable operating in a fast\-moving, resource\-constrained environment where you'll need to build, prioritize ruthlessly, and ship imperfect\-but\-functional solutions quickly.
- Exceptional cross\-functional communicator — you can translate between Sales leaders who want answers and engineers who need specifications.
- Experience in healthcare, dental, or vertical SaaS is a strong plus. Experience selling to SMBs or fragmented markets is highly valued.
- You are an active practitioner of AI and LLM tools — not just aware of them. You use them daily, have a point of view on what works, and can evaluate new tools with speed and rigor.
- You stay at the frontier of AI in go\-to\-market: you follow the space, experiment early, and translate new capabilities into operational advantage for the revenue team.
What We Offer
- Competitive base salary \+ performance bonus
- Equity (stock options) with refresh opportunity tied to company milestones
- Comprehensive health, dental, and vision coverage
- Flexible PTO policy
- Remote\-first environment, with preference for candidates in Los Angeles, Salt Lake City / Lehi, or New York City
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 PEARL, 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. Director-level AI roles across all categories have a median of $244,288.
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.
PEARL AI Hiring
PEARL has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $212,800 across 317 tracked positions. That's 16% above the national 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 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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