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About This Role
LiveRamp is the data collaboration platform of choice for the world’s most innovative companies. A groundbreaking leader in consumer privacy, data ethics, and foundational identity, LiveRamp is setting the new standard for building a connected customer view with unmatched clarity and context while protecting precious brand and consumer trust. LiveRamp offers complete flexibility to collaborate wherever data lives to support the widest range of data collaboration use cases—within organizations, between brands, and across its premier global network of top\-quality partners.
Hundreds of global innovators, from iconic consumer brands and tech giants to banks, retailers, and healthcare leaders turn to LiveRamp to build enduring brand and business value by deepening customer engagement and loyalty, activating new partnerships, and maximizing the value of their first\-party data while staying on the forefront of rapidly evolving compliance and privacy requirements.
The Sales Director, Brands is responsible for driving new business and expanding strategic relationships across enterprise brands and their agency partners. This role focuses on selling the full LiveRamp portfolio, including identity, data collaboration, activation, and measurement solutions, to support full\-funnel, media\-driven outcomes.
This is a highly strategic, client\-facing role at the intersection of marketing, media, data, and technology. The ideal candidate brings experience selling into enterprise marketing organizations and agencies, with a strong understanding of how brands leverage first\-party data across activation, insights, and measurement to drive performance.
Success in this role requires the ability to navigate complex ecosystems, including brands, agencies, and retail media networks, and position LiveRamp as a critical enabler of connected, outcome\-based marketing strategies.
You will:
- Consistently exceed quarterly and annual revenue targets across a defined set of enterprise accounts
- Build and maintain a strong pipeline across both brand\-direct and agency\-driven opportunities
- Develop and execute strategic account plans that align to clients’ full\-funnel marketing and media objectives
- Lead complex, consultative sales cycles spanning marketing, media, data, and analytics stakeholders
- Engage senior stakeholders across brands and agencies (CMO, Chief Media Officer, CDO, CIO, VP\-level)
- Position LiveRamp solutions in the context of media performance, audience strategy, and measurement outcomes
- Partner cross\-functionally to deliver tailored solutions across identity, activation, and measurement use cases
- Drive awareness and demand through industry events (e.g., Cannes, Shoptalk, NRF), networking, and partnerships
- Navigate multi\-party deal cycles, including coordination across brands, agencies, and platform partners
- Manage forecasting, pipeline hygiene, and account planning with a high degree of accuracy
- Ensure long\-term client success, expansion, and retention
About you:
- 5\+ years of enterprise B2B sales experience, preferably in adtech, martech, or data platforms
- Proven track record of exceeding revenue targets in complex, multi\-stakeholder sales environments
- Experience selling into enterprise marketing organizations and/or agencies
- Strong understanding of the digital media ecosystem, including activation, audience strategy, and measurement
- Experience selling solutions tied to marketing performance, media outcomes, or data\-driven decisioning
- Demonstrated ability to navigate and close deals involving brands, agencies, and platform partners
- Strong consultative selling skills with the ability to translate technical capabilities into business outcomes
- Experience managing complex deal cycles, including RFPs, MSAs, and commercial negotiations
- Executive presence with the ability to engage and influence senior stakeholders
Nice to have:
- Experience working with or selling into retail media networks, DSPs, or walled gardens
- Familiarity with identity resolution, clean rooms, or cross\-channel measurement solutions
- Experience supporting full\-funnel media strategies (brand \+ performance \+ commerce)
- Background working with holding companies or major agency networks
- Experience within Retail \& CPG
*This is a hybrid, 2 days a week in\-office position in San Francisco or New York City.*
The approximate annual base compensation range is $117,500 to $160,000\. The actual offer, reflecting the total compensation package and benefits, will be determined by a number of factors including the applicant's experience, knowledge, skills, and abilities, geography, as well as internal equity among our team.
Benefits:
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- People: Work with talented, collaborative, and friendly people who love what they do.
- Fun: We host in\-person and virtual events such as game nights, happy hours, camping trips, and sports leagues.
- Work/Life Harmony: Flexible paid time off, paid holidays, options for working from home, and paid parental leave.
- Comprehensive Benefits Package: LiveRamp offers a comprehensive benefits package designed to help you be your best self in your personal and professional lives. Our benefits package offers medical, dental, vision, life and disability, an employee assistance program, voluntary benefits as well as perks programs for your healthy lifestyle, career growth and more.
- Savings: Our 401K matching plan—1:1 match up to 6% of salary—helps you plan ahead. Also Employee Stock Purchase Plan \- 15% discount off purchase price of LiveRamp stock (U.S. LiveRampers)
- RampRemote: A comprehensive office equipment and ergonomics program—we provide you with equipment and tools to be your most productive self, no matter where you're located
More about us:
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*LiveRamp’s mission is to connect data in ways that matter, and doing so starts with our people. We know that inspired teams enlist people from a blend of backgrounds and experiences. And we know that individuals do their best when they not only bring their full selves to work but feel like they truly belong. Connecting LiveRampers to new ideas and one another is one of our guiding principles—one that informs how we hire, train, and grow our global team across nine countries and four continents. Click* *here* *to learn more about Diversity, Inclusion, \& Belonging (DIB) at LiveRamp.*
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LiveRamp is an affirmative action and equal opportunity employer (AA/EOE/W/M/Vet/Disabled) and does not discriminate in recruiting, hiring, training, promotion or other employment of associates or the awarding of subcontracts because of a person's race, color, sex, age, religion, national origin, protected veteran, disability, sexual orientation, gender identity, genetics or other protected status. Qualified applicants with arrest and conviction records will be considered for the position in accordance with the San Francisco Fair Chance Ordinance.
We use automated decision systems (ADS) as part of our recruitment and hiring process. If you require an accommodation or believe that the use of an ADS may create a barrier to your application or participation in the hiring process due to a disability or other protected characteristic, please let us know. We are committed to providing reasonable accommodations and ensuring an equitable hiring experience for all candidates.
California residents: Please see our California Personnel Privacy Policy for more information regarding how we collect, use, and disclose the personal information you provide during the job application process.
To all recruitment agencies: LiveRamp does not accept agency resumes. Please do not forward resumes to our jobs alias, LiveRamp employees or any other company location. LiveRamp is not responsible for any fees related to unsolicited resumes.
Salary Context
This $117K-$160K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At LiveRamp, 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. This role's midpoint ($138K) sits 17% below the category median. Disclosed range: $117K to $160K.
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.
LiveRamp AI Hiring
LiveRamp has 4 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Positions span New York, NY, US, San Francisco, CA, US. Compensation range: $147K - $263K.
Location Context
AI roles in New York pay a median of $200,000 across 1,670 tracked positions. That's 9% 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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