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About This Role
About Petfolk
Petfolk is a veterinary care company founded by Dr. Audrey Wystrach. We run modern clinics, a 24/7 virtual care app, and a connected care model that pet parents actually love. Our NPS sits above 90\. Newsweek put us in their top five Most Loved Workplaces globally. We've raised north of $116M from Deerfield Management, White Star Capital, Movendo Capital, and others.
The Opportunity
We’re hiring an AI Strategy Lead to own the deployment, launch, and ongoing optimization of conversational AI across Petfolk’s virtual care business. This person will sit inside the Virtual Care and Customer Service function and report directly to the VP of Medical Strategy and Development.
By the time you start, the conversational AI platform will already be selected and in deployment. Your first job is to get deeply fluent in the platform’s tooling, functionality, and every protocol driving customer interactions across phone, SMS, email, and chat. From there, you’ll own the launch, monitor performance against real success criteria, and iterate relentlessly — refining workflows, guardrails, escalation paths, and prompts until the system is genuinely excellent.
Over time, this role expands. You’ll take on broader ownership of virtual care analytics — interaction data, staffing patterns, service levels, handle times — and use that analysis to identify where AI can create more value and where more traditional operational changes are the better lever. You’ll build business cases, model ROI, and partner with Engineering, Brand, Operations, and Medical to put improvements in place.
This is not a role for someone who wants to observe or advise. It’s for someone who looks at how things are currently working, figures out what’s slowing it down, and fixes it. You’ll own work end to end — from figuring out the problem to putting something in place and making sure it actually gets used.
Success in this role is simple: The virtual care team moves faster, customers get better service, and the AI systems you manage deliver measurable improvements in quality, efficiency, and scalability.
What You’ll Do
- Take full ownership of the conversational AI platform already in deployment — learn the tooling, functionality, and interaction protocols inside and out, then drive it through launch and beyond.
- Define and document use cases, success criteria, escalation rules, and guardrails for AI\-enabled customer interactions across phone, SMS, email, and chat.
- Lead testing efforts in close partnership with operational, medical, engineering, brand, and vendor partners. Evaluate performance against defined quality, customer experience, and business success measures.
- Own the ongoing cycle of implementing, testing, evaluating, and iterating on AI\-supported workflows. Refine prompts, logic, escalation paths, and guardrails to drive the best possible outcomes. Prioritized by business impact.
- Identify and prioritize additional use cases where AI can improve customer experience, quality, efficiency, or scalability — and sequence future deployments based on operational need, business value, and feasibility.
- Analyze customer interaction data, workflow trends, staffing patterns, service levels, and handle times to surface broader improvement opportunities across virtual care.
- Build business cases and economic analyses for both AI and non\-AI initiatives, including expected impact on quality, customer satisfaction, efficiency, labor leverage, and return on investment.
- Establish reporting and review processes that provide clear visibility into performance across AI use cases. Define and monitor KPIs including adoption, containment, average handle time, CSAT, quality assurance scores, and implementation progress.
- Work cross\-functionally with Engineering, Brand, Operations, Medical, and other stakeholders to drive alignment, establish clear ownership and timelines, and ensure accountability across initiatives.
- Over time, expand into broader ownership of virtual care analytics, workforce management modeling, and capacity planning to improve operational decision\-making across the line of business.
Requirements
Must Have
- AI\-Native Builder. You don't just use AI tools. You build systems with them. You've put together workflows, automated pipelines, or created AI\-powered tools in a marketing or growth setting. You can show us what you've built and talk through why you made the choices you did.
- Claude and LLM Fluency. Real experience with Claude or comparable models. You get system prompt design, structured outputs, tool use, and evaluation loops. You treat prompts like code: you version them, test them, and improve them over time.
- Workflow Automation. You've built multi\-step automated workflows in N8N, Make, Zapier, or something similar. You've connected APIs, handled conditional logic, and dealt with things breaking in production. The specific tool matters less than the experience.
- Analytical Rigor. You can set up experiments, read results honestly, and build systems that track what matters. A/B testing, attribution, and performance dashboards across paid channels.
- Semi\-Technical Chops. Maybe you code, maybe you don't. Either way, you can read an API doc, write a script when you need to, debug an integration, and build with low\-code tools without asking engineering for help.
- Self\-Directed. Give you a goal and you'll find the path. You've worked in places where things moved fast, the roadmap was fuzzy, and you had to figure out what mattered most and go do it.
Strongly Preferred
- Agentic Systems Experience. You've built or prototyped agentic AI workflows where the system takes actions, calls tools, makes decisions, and routes work to a human when it should. Bonus points for Claude tool use or MCP experience.
- Custom MCP Development. You've built Model Context Protocol servers or integrations that plug LLMs into external tools and data sources.
- Background in top\-tier consulting, banking, product, or high\-growth startups
- AI Compliance Awareness. You pay attention to FTC guidelines and platform rules around AI\-generated content in ads. As a pet health brand, trust matters a lot to us, so this is more important here than it might be elsewhere.
- Prompt Engineering Discipline. You keep prompt libraries, run evals, version your system prompts, and treat the craft seriously. Beyond being good at using an LLM, you're good at building reliable systems on top of one.
Why Petfolk
- Be part of a cohort of AI Leads building something new across the whole company.
- Full autonomy and real ownership, reporting directly to the functional VP
- A company that's genuinely investing in AI, not just talking about it.
- Work that connects to something real. Better marketing means more pet parents find better care for their animals.
Work Environment
NYC HQ (4 days per week in\-office preferred).
This position requires minimal travel, up to 10%, to headquarters for in\-person meetings and to pet care centers to familiarize yourself with operations and ensure timelines are met.
Compensation:
$150,000 to $200,000 Annual Base
Apply with a portfolio of things you've built.
*What you've built matters more to us*
Salary Context
This $150K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Rippling, 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($175K) sits 20% below the category median. Disclosed range: $150K to $200K.
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.
Rippling AI Hiring
Rippling has 19 open AI roles right now. They're hiring across AI Product Manager, AI Software Engineer, AI/ML Engineer, Data Engineer. Positions span Remote, US, New York, NY, US, San Francisco, CA, US. Compensation range: $60K - $330K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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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