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About This Role
Ready to be a Titan?
ServiceTitan is looking for a Lead Salesforce Engineer to join our team. In this role, you'll own the architecture, development, and ongoing evolution of our Salesforce platform — with a deep focus on CPQ and Billing — while collaborating with stakeholders across Sales, Revenue Operations, and Engineering. You'll leverage AI tools to accelerate delivery, raise quality, and push the boundaries of what our Salesforce org can do.
What You'll Do
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- Partner directly with Marketing, Sales, SDR, RevOps, and GTM Systems leaders to identify, prioritize, and define high\-value AI use cases.
- Translate business problems into scalable technical solutions rather than simply implementing predefined requirements.
- Own solution delivery from discovery, technical design, and prototyping through production deployment, adoption, monitoring, and optimization.
- Design and build production\-grade applications using Python, Azure, Salesforce, LLMs, RAG architectures, APIs, and third\-party AI tools.
- Design reusable agentic solutions spanning orchestration, MCP servers and connectors, live system tool calling, conversational experiences across Slack, embedded chat, APIs, and Salesforce, with standardized connector libraries, deployment patterns, and Dev, UAT, and Production promotion paths.
- Develop AI solutions for:
+ Account and prospect research
+ Lead qualification and prioritization
+ Personalized seller and SDR outreach
+ Seller productivity and workflow automation
+ Opportunity intelligence and next\-best actions
+ Pipeline inspection and deal\-risk identification
+ Forecast management and accuracy
+ Guided selling and knowledge retrieval
- Integrate AI capabilities into Salesforce and the broader GTM technology ecosystem through APIs, platform events, automation, and embedded user experiences.
- Establish evaluation practices for AI quality, groundedness, relevance, task completion, hallucination risk, latency, cost, reliability, and business impact.
- Implement appropriate security, access, privacy, monitoring, and responsible\-AI controls.
- Evaluate Salesforce\-native, Azure\-native, and third\-party AI solutions and recommend whether to buy, build, or integrate.
- Convert successful solutions into reusable services, components, templates, evaluation datasets, and architecture patterns.
- Lead technical design and code reviews, mentor engineers, and help establish engineering standards for GTM AI solutions.
- Partner with business teams to drive workflow redesign, adoption, and measurable business outcomes.
What We're Looking For
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- Typically 10\+ years of software engineering, solutions engineering, systems engineering, or technical architecture experience.
- Experience building AI\-powered solutions for Sales, SDR, Revenue Operations, or pre\-sales organizations.
- Demonstrated experience designing, building, and operating production applications using Salesforce, Integration platforms, Python and Microsoft Azure.
- Strong hands\-on experience with LLM\-based applications, RAG, embeddings, vector or hybrid retrieval, prompt and context management, structured outputs, tool calling, and AI evaluation.
- Strong Salesforce expertise, including Salesforce Apex, Flow, Lightning Web Components, Platform Events, data models, APIs, integrations, automation, security, and custom development.
- Experience integrating Salesforce with cloud services, enterprise data sources, and third\-party platforms.
- Deep functional understanding of GTM processes, including:
+ Sales development and prospecting
+ Lead and account management
+ Opportunity lifecycle
+ Pipeline generation and inspection
+ Deal progression
+ Forecast management
+ Sales productivity
- Experience building production APIs, services, integrations, and data\-processing workflows.
- Strong understanding of cloud security, identity, access management, data privacy, observability, testing, and deployment practices.
- Experience with CI/CD, infrastructure as code, and automated testing.
- Proven ability to work directly with business stakeholders, uncover root problems, challenge assumptions, and recommend pragmatic solutions.
- Ability to independently navigate ambiguity and move from problem definition to production deployment.
- Strong written and verbal communication skills across technical, business, and executive audiences.
Preferred Qualifications
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- Experience with Azure OpenAI, Microsoft Foundry, Azure AI Search, Azure Functions, Azure Container Apps, API Management, Key Vault, and Azure Monitor.
- Experience with Vercel AI SDK, Mulesoft, Salesforce Agentforce and Data Cloud.
- Familiarity with modern AI orchestration frameworks, model gateways, vector databases, and evaluation platform
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: $163,400 USD \- $245,000 USD Applicable for: CA, CT, DC, MD, MA, NJ, NY, VA, and WA Zone 2: $152,600 USD \- $229,000 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 $152K-$245K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At ServiceTitan, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($198K) sits 7% below the category median. Disclosed range: $152K to $245K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
ServiceTitan AI Hiring
ServiceTitan has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in CA, US. Compensation range: $245K - $245K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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