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About This Role
Who we are:
Motive empowers the people who run physical operations with tools to make their work safer, more productive, and more profitable. For the first time ever, safety, operations and finance teams can manage their drivers, vehicles, equipment, and fleet related spend in a single system. Combined with industry leading AI, the Motive platform gives you complete visibility and control, and significantly reduces manual workloads by automating and simplifying tasks.
Motive serves nearly 100,000 customers – from Fortune 500 enterprises to small businesses – across a wide range of industries, including transportation and logistics, construction, energy, field service, manufacturing, agriculture, food and beverage, retail, and the public sector.
Visit gomotive.com to learn more.
### About the Role:
At Motive, the Growth Marketing organization is responsible for driving efficient, scalable revenue growth across the full customer journey—from awareness to pipeline to expansion. We are focused on building high\-performance, measurable growth systems, not just campaigns.
We are seeking a highly strategic and execution\-oriented Senior Manager, Paid Social \& Programmatic to own and scale paid media across paid social and programmatic channels (e.g., LinkedIn, Meta, 6sense, Viant).
This role is critical to transforming paid media into a predictable, full\-funnel revenue engine. You will partner closely with Demand Generation, Lifecycle Marketing, Sales, and Marketing Ops to ensure paid media is fully integrated into Motive’s GTM strategy and driving measurable pipeline impact.
The ideal candidate combines:
- Strong performance marketing fundamentals
- First\-principles thinking and problem solving
- Creative and messaging intuition
- Deep analytical rigor and experimentation mindset
- Fluency with AI tools to drive speed, scale, and innovation
This role reports to the Director of Growth Marketing and is remote (US\-based).
### What You'll Do:
### Strategic Leadership \& Planning
- Own and define Motive’s paid media strategy across social and programmatic channels
- Translate Demand Gen priorities into scalable, full\-funnel paid media plans
- Apply first\-principles thinking to unlock growth opportunities
- Establish clear hypotheses, testing roadmaps, and success metrics
- Drive alignment across Demand Gen, Sales, and Lifecycle
### Channel Ownership \& Execution
- Own end\-to\-end execution across:
- + Paid Social (LinkedIn, Meta)
+ Programmatic / ABM platforms (6sense, Viant, DSPs)
- Activate and scale Demand Gen campaigns with alignment on:
- + Target accounts and segments
+ Messaging and creative
+ Timing and objectives
- Build and scale full\-funnel programs:
- + Account\-based prospecting
+ Retargeting and nurture
+ Conversion and pipeline acceleration
- Lead a video\-first strategy to improve engagement and conversion
- Optimize content syndication programs for lead quality
- Continuously refine audience strategies (ICP, intent, behavioral signals)
- Drive optimization across creative, targeting, bidding, and budget
### Creative Strategy, AI \& Innovation
- Partner with Demand Gen, Brand, and Content teams on creative development
- Leverage AI tools to:
- + Accelerate ideation and iteration
+ Generate messaging and format variations
+ Improve workflow efficiency
- Establish structured experimentation across formats (video, display, native, social)
- Continuously test and evolve messaging
### Team Leadership \& Development
- Manage Paid Media Specialist (1 FTE) and agency partners
- Set a high bar for ownership, accountability, and performance
- Build a culture of testing and continuous improvement
- Ensure operational rigor across planning, execution, and reporting
### Analytics, Measurement \& Optimization
- Define KPIs aligned to pipeline, revenue, and efficiency
- Build reporting frameworks tying paid media to business outcomes
- Lead experimentation strategy:
- + Creative and audience testing
+ Budget and channel mix testing
+ Incrementality testing (holdouts, geo tests, lift studies)
- Partner with Analytics and Marketing Ops on attribution and data integrity
- Provide insights to inform budget and strategy decisions
### Cross\-Functional Collaboration
- Partner with Demand Generation to activate campaign strategy
- Collaborate with Lifecycle Marketing on retargeting and nurture
- Align with Sales and SDR teams on account targeting
- Work with Marketing Ops and Analytics on measurement and reporting
- Partner with Product Marketing and Brand on messaging consistency
### What We're Looking For:
### Education \& Experience
- Bachelor’s degree in Marketing, Business, or related field
- 7–10\+ years in performance marketing (paid social \+ programmatic)
- Proven track record driving pipeline and revenue impact
- Experience in Demand Gen / campaign\-driven environments
- 2–4\+ years managing teams or agencies
### Technical Competencies
- Deep expertise in:
+ Paid Social (LinkedIn, Meta)
+ Programmatic platforms (6sense, Viant, DSPs
- Strong understanding of:
+ Demand generation and ABM strategies
+ Audience targeting and intent data
+ Video and display best practices
+ Content syndication programs
- Experience with:
+ Google Analytics, Tableau, Amplitude
+ Salesforce, Marketo, marketing data infrastructure
- Knowledge of:
+ Experimentation frameworks
+ Incrementality and attribution
- Comfort using AI tools for marketing workflows
Preferred Qualifications
----------------------------
- Experience with ABM strategies and platforms
- Background in full\-funnel marketing and attribution modeling
- Experience managing large\-scale budgets
- Strong experimentation and growth marketing background
- Experience marketing to enterprise audiences (complex buying groups, long sales cycles, multi\-stakeholder deals)
*Pay Transparency*
*Your compensation may be based on several factors, including education, work experience, and certifications. For certain roles, total compensation may include restricted stock units. Motive offers benefits including health, pharmacy, optical and dental care benefits, paid time off, sick time off, short term and long term disability coverage, life insurance as well as 401k contribution (all benefits are subject to eligibility requirements). Learn more about our benefits by visiting**Motive Perks \& Benefits*
The base compensation range for this role is:
$155,000 \- $186,000 USD
*Creating a diverse and inclusive workplace is one of Motive's core values. We are an equal opportunity employer and welcome people of different backgrounds, experiences, abilities and perspectives.*
*Please review our Candidate Privacy Notice* *here**.*
*UK Candidate Privacy Notice* *here**.*
*The applicant must be authorized to receive and access those commodities and technologies controlled under U.S. Export Administration Regulations.* *It is Motive's policy to require that employees be authorized to receive access to Motive products and technology.*
Salary Context
This $155K-$186K 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 Motive, 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. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $155K to $186K.
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.
Motive AI Hiring
Motive has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span San Francisco, CA, US, Remote, US. Compensation range: $186K - $186K.
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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