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About This Role
The Principal, Sales AI Strategy and Execution is a senior individual contributor on Tipalti's Data team and supporting the Sales organization. Sitting at the intersection of AI strategy and Sales revenue execution, this role is a key driver of Tipalti's AI\-led growth transformation \- the person who continuously transforms the AI\-powered tools and workflows that enable our Sales Development, Sales, Solutions Consulting, Account Management, and Alliances teams to deliver a world\-class buying and selling experience at every stage of the customer journey.
You close the gap between what a world\-class data and AI team can build and the associated workflows for optimized execution and results. You bring enough technical depth to configure intelligently, and enough Sales domain expertise to translate field needs into precise requirements without losing anything in translation. You collaborate closely with Tipalti's data and AI teams cross\-functionally, while maintaining the ability to drive the AI\-led growth roadmap forward independently. You own the roadmap, build the timelines, and measure and own the outcomes.
You will operate core elements of Tipalti's AI\-powered Sales tech stack, whether that means orchestrating and configuring new capabilities, designing workflows across the customer journey, or extending existing platforms to unlock new value for the field. You will bring our AI use case roadmap to life in collaboration with our AI teams alongside our Sales Excellence team and in collaboration with key stakeholders across the Sales organization, and over time, a foundational force in Tipalti's AI\-led growth transformation.
Why Join Tipalti?
Tipalti is the AI\-powered platform for finance automation, elevating how finance teams operate in the global economy. We empower our customers to scale faster and smarter by removing the complexities of doing global business and accelerating their finance operations efficiency.
Our platform provides a comprehensive suite of finance automation solutions designed for mid\-market businesses across accounts payable, global payouts, procurement, employee expenses, corporate cards, supplier management, tax compliance, and treasury.
At Tipalti, we pride ourselves on our collaborative culture, the quality of our product, and the capabilities of our people. Tipaltians are passionate about the work they do and keen to get the job done. Our culture ensures everyone checks their egos at the door and stands ready to reach for success together.
In This Role, You'll Be Responsible For:
Tipalti's AI\-augmented sales ambition is not a collection of standalone features. It is a unified system that works across teams, workflows, and stages of the buying and selling journey to create a seamless, world\-class experience for both the buyer and the seller. Your objective is to architect and optimize the system and execute on this vision for accelerated scalable growth that is both effective and efficient
- Manage AI\-powered Sales tools and related processes, whether built internally, configured from third\-party platforms, or delivered in partnership with our data and AI teams, and be accountable for their ongoing performance, evolution, and measurable field impact
- Approach our Sales organization with a systems mindset, designing for interoperability across the full Sales stack, breaking down workflow silos, and ensuring data, context, memory, and intelligence flow seamlessly across the buying and selling journey rather than accumulating in disconnected tools
- Coordinate the build and continuously evolve our capabilities for Sales teams
- Develop deep platform proficiency and configure the workflows, agents, automations, and integrations that translate AI capabilities into daily jobs\-to\-be\-done for AEs, SDRs, SCs, AMs, and Alliance Managers
- Regularly test and deploy workflow automations, tool integrations, and AI\-powered processes, API connections, and native capabilities across the Sales stack. Including Salesforce, Clari, Clari Copilot, ZoomInfo, Actively, Hyperbound, Openprise, HockeyStack, Gainsight, 6Sense, Outreach, and Sumble
- Evaluate emerging tools and capabilities with a complete customer life cycle mindset — configuring proof\-of\-concepts, validating with the field, and scaling what works — while maintaining documentation that keeps the system coherent and maintainable as it grows
Bridge the Field and Drive the Roadmap
You are equally at home talking to an AE about why a workflow isn't hitting right and talking to a data engineer about API schema design. You surface what the field needs, translate it into what gets built, and own the roadmap sequences
- Work directly with AEs, SDRs, SCs, AMs, and Alliance Managers to surface workflow friction, unmet needs, and AI use case opportunities, translating field insights into precise technical requirements and prioritized roadmap decisions
- Operate as the Sales org's internal voice for AI tools, owning the use case intake process, backlog prioritization, and delivery accountability across teams' motions
- Collaborate closely with Tipalti's data and AI teams on shared use case design, data quality requirements, and architectural decisions, serving as the primary voice of the Sales field in cross\-functional AI discussions while maintaining the ability to configure and deploy independently on Sales\-specific use cases and systems
- Coordinate cross\-functionally with Sales Ops, Enablement, Marketing, Operations, Customer Success, Finance, and Alliance stakeholders to ensure use cases align to the broader revenue motion and don't create friction at journey handoff points
- Represent the AI\-led growth roadmap to Sales leadership — translating technical progress into field impact and pipeline implications, and maintaining a transparent view of what is shipping, what is delayed, and why
Measure, Iterate, and Report
- Define success metrics for every tool, workflow, and AI capability deployed — before build and report on the related output metrics
- Analyze performance data, AI output quality, and field adoption signals to continuously improve outcomes across the buying and selling journey
- Deliver regular reporting to Sales leadership on AI tool performance, workflow adoption, and revenue impact across all supported Sales motions
About You
- 10\+ years of experience in a Sales organization, with at least 4\+ years of experience in a technical, systems, hybrid Sales/builder role. You understand how the field works because you've been close to it, and you know how to build systems, workflows, and use cases that the field actually uses
- Proven technical proficiency: API fluency (reading documentation, configuring integrations, configuring webhooks, troubleshooting data flows), and hands\-on experience deploying AI\-powered Sales systems in production (i.e., not just proof\-of\-concepts)
- Experience owning AI\-powered tools or products in production. You know what it means to be accountable for a live system's performance, iterate on it continuously, and hold it to a measurable standard
- Deep understanding of Sales Development, Sales, Solutions Consulting, Account Management, and Alliances motions, including prospecting, pipeline management, deal execution, partner\-sourced pipeline, and expansion workflows
- Proficiency across the modern Sales stack: Salesforce, Clari, Clari Copilot, ZoomInfo, Actively, Hyperbound, Openprise, HockeyStack, Snowflake, Gainsight, Outreach, and Sumble; and hands\-on experience with AI and automation platforms including Claude, Gemini, Workato iPaaS, Gumloop, n8n, Zapier, Make, or equivalent
- Strong communicator who operates fluently across technical and non\-technical stakeholders with the ability to prioritize and sequence a technical roadmap under real\-world constraints, and represent complex work clearly to senior leadership
- Experience in B2B SaaS or a complex, multi\-product Sales environment is highly preferred
- Highly self\-directed with a bias toward action: you identify what needs to change before you're asked, and you own the outcome.
Base Salary Range: $165,000 \- 203,500 USD annually.
Our Mission
Our mission is to elevate how finance teams operate in the global economy. We empower our customers to scale faster and smarter by removing the complexities of doing global business and accelerating their finance operations efficiency. We are the AI\-powered platform that automates finance.
Tipalti is fueled by a commitment to our customers and a desire to build lasting connections. Our client portfolio includes high\-velocity businesses such as Amazon Twitch, GoDaddy, Roku, WordPress.com, and ZipRecruiter. We work hard for our 99% customer retention rate which is built on trust, reliability and innovation. Tipalti means we handled it" \- a mission to which we are constantly committed.
Accommodations
Tipalti champions inclusive teams, in which every voice counts. We are committed to recruiting diverse candidates with varied personal experiences and abilities. We welcome applications from candidates belonging to historically underrepresented or disadvantaged groups, and maintain an equitable Talent Acquisition process that is free from discrimination.
As an equal opportunities employer, Tipalti complies with employment and human rights laws across the various jurisdictions in which we operate. Should you require reasonable adjustments or accommodations during the recruitment process, including access to alternate formats of materials, meeting spaces, or other accommodations that could better enable your full participation, please reach out to [email protected] for assistance.
AI Use
We may use artificial intelligence and automated systems (collectively "AI") to screen, assess, and select candidates during our recruitment process. This includes resume screening, skills assessment, and candidate matching. You have the right to request human review of any automated decision. For more information about how we collect and use personal data and information during recruitment, please refer to our Job Candidate Privacy Notice. For additional questions about our use of AI during our recruitment process, you can contact [email protected].
Privacy
We are committed to protecting the privacy interests of job applicants and candidates. For more information about our privacy practices during our Talent Acquisition process, please refer to our Job Candidate Privacy Notice below:
Job Candidate Privacy Notice \| Tipalti
www.tipalti.com/privacy/job\-candidate\-privacy\-notice/
Salary Context
This $165K-$203K 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 Tipalti, 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 ($184K) sits 14% below the category median. Disclosed range: $165K to $203K.
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.
Tipalti AI Hiring
Tipalti has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $203K - $203K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% 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 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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