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About This Role
Who we are:
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Tinuiti is the largest independent full\-funnel marketing agency in the U.S. across the media that matters most, with $4 billion in digital media under management and more than 1,200 employees. Built for marketers who demand growth and accountability, Tinuiti unites media and measurement under one roof to eliminate waste—the biggest growth killer of all—and scale what works. Its proprietary technology, Bliss Point by Tinuiti, reveals the truth around growth and waste, and how to capitalize on it. With expert teams across Commerce, Search, Social, TV \& Audio, and more, Tinuiti delivers measurable results with brutal simplicity: Love Growth. Hate Waste.We support 100% remote work for this role!We’d love to hear from you if:
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Research shows that while men apply to jobs when they meet an average of 60% of the criteria, women and other marginalized folks tend to only apply when they check every box. So if you think you qualify, but don't necessarily meet every single point on the job description, please still get in touch.
As the Senior Director, Lifecycle Marketing, you will own the strategic vision and business outcomes for Tinuiti’s CRM and Retention offering. You will be the primary stakeholder for our most complex lifecycle engagements, ensuring that email, SMS, and loyalty programs are seamlessly integrated into the broader media mix to eliminate waste and drive LTV. You will own the vision for how our clients capture, nurture, and monetize their customer databases through advanced segmentation and automation strategy, while identifying new opportunities for lifecycle to shape retention strategy across the full customer journey and activate CRM intelligence beyond owned channels. You will lead a specialized department of lifecycle experts, translating deep CRM, ESP, and first\-party data expertise,, platform capabilities, competitive CRM landscapes, and Bliss Point insights into world\-class retention roadmaps.Client Centric* Strategic Roadmap: Lead the vision for our most complex enterprise clients, moving them beyond automation into sophisticated, data\-driven personalization and predictive lifecycle journeys, and leveraging first\-party data from client CRMs to build audiences, power decisioning, and drive growth both within lifecycle programs and across other channels.
- Consultative Growth: Act as the primary subject matter expert during high\-level business reviews, identifying untapped revenue within a client's existing database.
- Retention Excellence: Build and maintain "sticky" client relationships through proactive communication and a deep understanding of their unique business priorities.
Product Led* Feedback Loops: Partner with the Product team to bridge the gap between client CRM needs and our proprietary tech; sponsor improvements that drive "Hate Waste" efficiency.
- AI \& Automation First: Champion the use of AI for dynamic content, predictive churn modeling, and streamlining internal campaign operations to drive "Hate Waste" efficiency.
- Platform Innovation: Maintain executive\-level relationships with major ESP/CDP partners (e.g., Braze, Klaviyo, Salesforce Marketing Cloud) to ensure Tinuiti stays at the forefront of AI and other beta features.
- Bliss Point Integration: Evangelize and drive the use of Bliss Point to measure the incremental lift of lifecycle touchpoints within a multi\-channel ecosystem.
- Lifecycle Capability Evolution: Conduct ongoing comprehensive assessments of Tinuiti’s current lifecycle capabilities across strategy, platforms, data architecture, and operating model. Identify opportunities to modernize and expand the offering, ensuring lifecycle marketing evolves alongside the broader full\-funnel media ecosystem.
- Go\-to\-Market Reimagination: Partner with executive leadership to re\-architect Tinuiti’s lifecycle go\-to\-market strategy—defining how CRM, retention, and owned media capabilities integrate with media, commerce, and measurement to create a differentiated full\-funnel offering for clients.
- Service Model Innovation: Develop scalable lifecycle service frameworks that connect CRM intelligence with paid media activation, ensuring lifecycle programs drive measurable business outcomes such as improved LTV, customer retention, and cross\-channel efficiency.
Data \& Measurement Driven* Advanced Analytics Narrative: Convert complex deliverability metrics and cohort analyses into clear narratives
- QA \& Compliance Governance: Oversee high\-severity deliverability escalations and global privacy compliance (CCPA/GDPR); ensure rigorous QA standards for complex automated triggers.
- Testing Rigor: Institutionalize a department\-wide culture of hypothesis\-driven testing, focusing on incrementality and predictive modeling over vanity metrics.
- Technical Proficiency: Bring hands\-on fluency in CRM and ESP architecture, including building and deploying complex, KPI\-based audiences, designing automation logic, and activating retention use cases both within and beyond ESPs.
The Tinuiti Way* Channel Connective Tissue: Be the Lifecycle liaison to other Tinuiti channels; drive connection points including how CRM data can be used for sophisticated "Lookalike" modeling and excluded\-audience efficiency.
- Standardized Playbooks: Codify the Tinuiti "Gold Standard" for lifecycle strategy, ensuring consistent excellence in QA, strategy, and reporting to ensure that all clients receive the same elite level of service regardless of their specific account team.
Owner Mindset* Growth: Lead the Lifecycle portion of new business pitches, crafting compelling narratives that win prospective clients. Be the primary owner of revenue growth for the Lifecycle channel via new business, existing client growth \& retention, and leveraging of partnerships, both external \& internal.
- Mentorship \& Meritocracy: Cultivate a high\-performance culture. You are responsible for the career mapping and professional growth of a diverse team of lifecycle experts.
Professional \& Technical Qualifications* 12\+ years of dedicated experience in Email, CRM, or Lifecycle Marketing, specifically leading enterprise\-level retention programs.
- Technical Mastery: Deep fluency in the "Retention Stack" (CDPs, ESPs, Loyalty Platforms) and experience with complex API integrations.
- Executive Presence: Proven ability to explain the ROI of first\-party data to a non\-technical CMO or CFO.
- Financial Fluency: Experience managing a specialized service P\&L, including staffing and margin management.
The annual base salary range for this role’s listed level is currently $148,000\-$168,000 plus performance bonus of 15%. Grade level and salary ranges are determined through interviews and a review of education, experience, knowledge, skills, abilities of the applicant, equity with other team members, and alignment with market data. We will provide more information on our benefits and equity upon requests. Sales roles are also eligible for incentive pay targeted up to or over 100% of the offered base salary (no cap). Disclosure as required by the Colorado Equal Pay for \> Equal Work Act, C.R.S. § 8\-5\-101 et seq.
FLSA Classification: ExemptWe are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.Benefits:
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Unlimited PTO: At Tinuiti, we believe you deserve time to rest, recharge, and enjoy life unplugged. When you prioritize time for yourself, you're able to bring your best self to work. That’s why we offer unlimited paid time off, a fully remote environment, and flexibility to take the time you need, when you need it. On top of that, we provide 20 paid holidays, including multiple long weekends, to ensure you have dedicated time to step away and disconnect. We're proud to offer above\-industry standard work\-life balance, consistently rated as one of the most loved benefits by Tinuitians year after year.Healthcare: Medical, Dental, Vision, Life \& Disability, Flex Spending AccountsRetirement: Match up to 4% of your contributions at 100%Perks and Wellness: Fringe, Forma, Unlimited Telemedicine and Teletherapy available at no cost, Thankful giving, EquityParental Leave: Birthing parents receive 16 weeks of leave with 100% pay (partners 12 weeks) after the birth or adoption of a child.Learning and Development: On\-demand learning, mentorship program, leadership and management development programs and resources*Disclaimer: This description has been designed to indicate the general nature and level of work performed by employees within this position. The actual duties, responsibilities, and qualifications may vary based on assignment or group. Tinuiti is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, gender, sexual orientation, gender identity or expression, religion, national origin, marital status, age, disability, veteran status, genetic information, or any other protected status.*
Salary Context
This $148K-$168K 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 Tinuiti, 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 ($158K) sits 5% below the category median. Disclosed range: $148K to $168K.
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.
Tinuiti AI Hiring
Tinuiti has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $70K - $168K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
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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