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About This Role
Ready to be a Titan?
The engineering organization at ServiceTitan is looking for a leader who will drive the AI/ML strategy to support our growth now and in the future. The ideal candidate will have experience shipping production\-grade ML and AI models — including large language models and reinforcement learning systems — and managing teams within a product company with the capability of bringing clear understanding of what the business goal behind the project is and anchoring the team's work in the context of the broader organizational strategy. They will work with business stakeholders across the company and support the creation of a global AI/ML competency.
What You'll Do:
- Drive overall model development life\-cycle from Ideation to operations, including fine\-tuning, evaluation, alignment, and deployment of LLMs and other production\-grade AI models
- Design and build enterprise\-grade AI agent systems and orchestration harnesses that reliably automate complex, multi\-step business workflows at scale
- Partner with Product organization to help identify the opportunities for AI/ML solutions, including agentic workflows, retrieval\-augmented generation (RAG), and reinforcement learning\-based systems
- Educate the organization how to leverage AI/ML to produce better customer outcomes
- Partner with Engineering and Product leadership to align on Roadmap and deliverables in order to deploy AI/ML models to production, with a focus on reliability, latency, cost optimization, and responsible AI practices
- Research and stay abreast of new technologies and industry trends in the rapidly evolving LLM and foundation model ecosystem. Incorporate new ideas that are well suited to advance the goals of ServiceTitan.
- Hire, manage, grow and retain a high performance team that can meet both present and future needs by identifying and leveraging AI/ML opportunities across the suite of the Product.
What You'll Bring:
- MS/Ph.D in Statistics, Applied Mathematics, Computer Science, Engineering, or similar quantitative discipline required.
- 7\+ years of experience in AI/ML leadership roles, with hands\-on experience shipping high impact, customer facing, production\-grade AI products
- Hands on knowledge of some of the most advanced methods in machine learning — including large language models, reinforcement learning (RL/RLHF), fine\-tuning, prompt engineering, retrieval\-augmented generation, and evaluation frameworks
- Expertise in SQL, previous experience with big data systems and modern ML infrastructure (e.g., GPU clusters, model serving platforms, vector databases)
- Coding experience in Python (strongly preferred), with familiarity in frameworks such as PyTorch, Hugging Face, vLLM, or similar
- Experience building and operating LLM\-powered features in production, including guardrails, evaluation pipelines, and cost/latency management
- Great communication skills including communicating complex concepts to business stakeholders
- Demonstrated ability to adapt to new technologies and learn quickly in a landscape that changes weekly
- Strong written and presentation communication skills
Be Human With Us:
Being human isn’t about checking every box on a list. It’s about the experiences we have, people we meet, and the perspectives we share. So, if you have the skills but are hesitant to apply because of your background, apply anyway. We need amazing people like you to help us challenge the conventional and think differently about the problems that we’re solving. We’re in this together. Come be human, with us.
Use of AI Technology:
We use technology, including automated and AI\-assisted tools, to support certain aspects of our recruitment process. These tools are designed to improve efficiency and enhance the candidate experience. AI tools are not used to make hiring decisions; all hiring decisions are made by our hiring teams.
What We Offer:
When you join our team, you’re not just accepting a job. You’re making a career move. Here’s how we’ll support you in doing some of the most impactful work of your career:
- Flextime, recognition, and support for autonomous work: Flexible time off with ample learning and development opportunities to continue growing your career. We offer a comprehensive onboarding program, leadership training for Titans at all levels, and other programs and events. Great work is rewarded through Bonusly, peer\-nominated awards, and more.
- Holistic health and wellness benefits: Company\-paid medical, dental, and vision (with 100% employer paid options and 90% coverage for dependents), FSA and HSA, 401k match, and telehealth options including memberships to One Medical.
- Support for Titans at all stages of life: Parental leave and support, up to $20k in fertility services (i.e. IUI and IVF), surrogacy, and adoption reimbursement, on demand maternity support through Maven Maternity, free breast milk shipping through Maven Milk, pet insurance, legal advisory services, financial planning tools, and more.
At ServiceTitan, we celebrate individuality and uniqueness. We believe that the convergence of fresh perspectives and experiences from all walks of life is what makes our product and culture so great. We strongly encourage people from underrepresented groups to apply. We do not discriminate against employees based on race, color, religion, sex, national origin, gender identity or expression, age, disability, pregnancy (including childbirth, breastfeeding, or related medical condition), genetic information, protected military or veteran status, sexual orientation, or any other characteristic protected by applicable federal, state or local laws.
ServiceTitan is committed to fair and equitable compensation for all of our employees. We thoughtfully consider a wide range of factors when determining individual compensation, which may change over time. We comply with all applicable minimum wage laws. For candidates in the United States, the good faith salary ranges estimate for this role is Zone 1: $239,300 USD \- $358,900 USD Applicable for: CA, CT, DC, MD, MA, NJ, NY, VA, and WA Zone 2: $223,600 USD \- $335,400 USD Applicable for: All other US locations. International Compensation for candidates residing outside the United States will vary by location and will be discussed during the hiring process. Actual compensation within a range is determined by factors including relevant experience, skill set, qualifications, and performance. In addition to base salary, our total compensation package includes an annual bonus, equity, and a holistic suite of benefits.
Salary Context
This $223K-$358K range is above the 75th percentile for AI Engineering Manager roles in our dataset (median: $202K across 12 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 4,133 AI roles we're tracking, AI Engineering Manager positions make up 0% of the market. At ServiceTitan, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Engineering Manager roles pay a median of $268,700 based on 42 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($291K) sits 8% above the category median. Disclosed range: $223K to $358K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and Research Engineer ($260,000). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
ServiceTitan AI Hiring
ServiceTitan has 2 open AI roles right now. They're hiring across AI Engineering Manager, AI Product Manager. Based in CA, US. Compensation range: $269K - $358K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 median).
Career Path
Common paths into AI Engineering Manager roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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