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About This Role
Directive Consulting is the performance marketing agency for SaaS and Tech companies. We use Customer Generation (a marketing methodology developed by us) which focuses on SQLs and Customers instead of traditional metrics like MQLs. We offer Paid Media, SEO/Content, CRO, and Video to our clients by creating comprehensive digital marketing strategies that allow our clients to hit their SQL targets, every time.
The Paid Strategists are a crucial, client\-facing role that is responsible for day\-to\-day client deliverables, creating and managing custom client strategies, and communicating both with clients and internal stakeholders on executing Paid Media deliverables. In this role, you will gain experience creating high\-performing Paid strategies for enterprise SaaS businesses.
*\*This role is listed internally as Senior Account Strategist, Paid Media*\*
Roles \& Responsibilities:
- Oversee and lead a collection of Paid Media accounts
- Serve as direct support to client contacts
- Weekly communication with any vendors or operational partners to ensure we're aligned on tasks being assigned, work quality, and any improvements we can make to that dynamic
- Drive referrals via client relationships and professional network
- Build strategies for clients each quarter
- Have the ability to create, maintain, and optimize budgets for paid media campaigns across multiple channels
- Deeply understand client positioning and unique value propositions
- Confirm lead routing is accurate within a CRM
- Understand the value of Programmatic campaigns
- Responsible for personal productivity and utilization
- Work directly with Associate Director to ensure internal and client goals are being achieved
- Execute and optimize PPC advertising campaigns across multiple platforms including Google Ads, Facebook Ads, and LinkedIn Ads, focusing on keyword research, ad copywriting, and audience targeting to maximize ROI
What You Offer:
- 3\+ years experience working at a performance/digital marketing agency
- Experience working specifically with B2B SaaS/tech clients in an agency setting
- Deep expertise across paid search and paid social advertising campaigns, such as Google Ads, Facebook Ads, LinkedIn Ads and others
- Proficiency in using advertising platforms and tools such as Google Ads, Facebook Business Manager, LinkedIn Campaign Manager
- Strong understanding of PPC principles, including keyword research, ad copywriting, bid management, and campaign optimization
- Ability to analyze campaign performance data using tools like Google Analytics, GA4, Excel, or other analytics platforms to make data\-driven decisions and optimize campaign performance
- Ability to drive results and measure via OCT
- Proven and measurable success with mid\-market or enterprise accounts
- A unique perspective on how to drive value for SaaS
- Ability to translate and articulate strategy and tell stories with data
- Equal parts competitive and curious; you’re a true problem solver
- You live on the cutting edge of the industry, always looking for opportunities to grow and share
- Ability to organize, prioritize and manage multiple projects simultaneously
- You’re quality\-obsessed and have not lost your soul for advertising
- Driven to stay ahead of industry trends, including actively learning how AI and automation can enhance marketing and operations.
- Travel to visit clients approximately once per year, per client or as needed
What Success Looks Like:
- You encompass our core values through every interaction; internally and externally
- Effectively manage approximately five mid\-tier and enterprise accounts
- Meet and exceed department level OKRs, such as client growth and goal attainment
- Build client trust and relationships that create consistent renewals
- Cross\-sell services that align with client goals and objectives
- Clearly communicate results with client point of contact and executives
- Exceptional decision making, as it relates to strategic direction for accounts
What We Offer:
We have a set living wage at Directive; The annual base salary range for this position based in the United States is $90,000\- $110,000 USD with potential for bonus eligibility. This salary range is an estimate, and the actual salary may vary based on Directive's compensation practices, job related skills, and depth of experience.
Medical, dental, vision plans, disability, and life insurance coverage for you and your family that fit your lifestyle
Including a 100% employer\-paid plan for you and a 50% employer contribution for your dependents
Benefits to Support the Whole Person:
Mental \- Access to certified therapists through Spring Health, membership to Headspace
Physical \- Gympass
Time Off \- Unlimited PTO (2\-week minimum), Paid Company Holidays, Your Birthday Off, End of Year Recharge (Closed December 24 \- January 1\), Paid Parental Leave
Financial \- Traditional and Roth 401(k) with a 3% company match
Bonus \- Annual bonus based on tenure, which scales in total amount over time
Annual Anniversary Trip with peers and executive leadership for fun and entertainment!
Work Environment Requirements:
As a remote\-first company, you’ll have the ability to work from anywhere in the US, with the option to enjoy our state\-of\-the\-art offices in Irvine, California. For some positions, as posted, we will accommodate global opportunities where we have established businesses, including Canada, the UK, and Australia. For global locations, you must have established and current work authorization and permanently reside in that country.
This role has the opportunity to operate 100% virtually from your home office. We primarily collaborate with our colleagues through virtual meetings (Zoom), and Slack. In this role, you will be required to operate a laptop computer (PC or Mac available), computer software platforms, and other office productivity tools as necessary. Due to the nature of this role, you must be able to remain stationary for extended periods, must be able to observe and interpret written and/or verbal communication, must have reliable internet access, and a professional background.
To perform this job successfully, an individual must be able to perform each essential job duty satisfactorily. Reasonable accommodations may be made to enable qualified individuals with disabilities to perform essential job functions.
If you require reasonable accommodations in completing this application, interviewing, completing any pre\-employment testing, or otherwise participating in the employee selection process, direct your inquiries to careers@directiveconsulting.com.
Additional Information:
At Directive, one of our core values is People First. We’re committed to fostering a more diverse and inclusive culture in the digital landscape. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status. \#LI\-CV1
Salary Context
This $90K-$110K 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 Directive, 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. This role's midpoint ($100K) sits 40% below the category median. Disclosed range: $90K to $110K.
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.
Directive AI Hiring
Directive has 216 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Hoboken, NJ, US, Stamford, CT, US, Charlotte, NC, US. Compensation range: $95K - $150K.
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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