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About This Role
ABOUT US
Recruitics Talent Intelligence and Acquisition Platform unites data, AI, and automation that make modern hiring possible — helping Enterprise organizations predict what’s coming next, attract the right talent, and convert talent anywhere.
We’re big believers in teamwork, curiosity, and doing work that actually makes an impact. If you love solving problems, thinking creatively, and helping companies build amazing teams, you’re going to love it here.
OVERVIEW
The Director of Applied AI is a cross\-functional role with direct visibility to the executive team. You'll work alongside the CPO, COO, and CEO in a standing weekly meeting where you'll present recommendations and solutions. With executive approval, you'll lead implementation across product, client services, operations, and go\-to\-market functions.
This is not a strategy role or a Chief of Staff role. It is a hands\-on operating role focused on execution: identifying friction in our business, designing solutions, and shipping the changes that improve how Recruitics operates day\-to\-day. You'll spend your first month embedded across teams, learning how the business runs today. After that, you'll work in weekly cycles — diagnose, recommend, implement, measure.
This role assumes meaningful, hands\-on experience using AI tools to compound your own output. We're not looking for general familiarity with AI — we're looking for someone who has already built workflows that replace manual work and saves measurable time each week. In the application process, we'll ask you to share a specific example of an AI\-powered workflow you've designed and shipped.
RESPONSIBILITIES
- Partner with the CPO, COO, and CEO to identify and prioritize operational opportunities across the company
- Diagnose workflows across client services, implementation, product operations, and go\-to\-market — and recommend changes with clear solutions
- Lead implementation of approved changes, working directly with functional leaders and their teams
- Design and build automated workflows using modern tooling, including AI\-powered solutions, to reduce manual work and increase team capacity
- Measure the impact of changes and iterate based on what the data shows
- Build durable operating systems — documentation, dashboards, and workflows that outlast any single project
REQUIREMENTS
- 7\+ years of experience in business operations, strategy and operations, internal consulting, or a similar cross\-functional role
- Proven track record of identifying inefficiencies in complex organizations and successfully leading implementation of operational improvements
- Hands\-on experience building automated workflows. You should be comfortable with tools such as Claude, n8n, Zapier, Make, Retool, or similar platforms, and able to point to specific automations you've built and shipped
- Strong systems thinking — you understand how processes, teams, and tools interact, and you can spot dependencies others miss
- Comfortable working directly with executives and presenting recommendations to senior leadership on a recurring basis
- Strong written and verbal communication skills
- Self\-directed and outcome\-oriented — you can identify your own priorities and execute without close supervision
Preferred Qualifications:
- Experience in a services\-to\-SaaS transformation, or in scaling a company through a significant operational evolution
- Background in recruitment marketing, HR technology, or adjacent industries
- Experience building with the Anthropic API, OpenAI API, or similar developer\-facing AI tools
- Familiarity with modern data and analytics stacks (Looker, dbt, etc.)
WHY JOIN US?
At Recruitics, you’ll have the opportunity to take full ownership of design within a cutting\-edge team focused on leveraging AI and data to transform the recruitment marketing landscape. You’ll be joining a creative and collaborative environment where your work will directly impact our clients’ success and you will have opportunities for career growth, professional development, and continuous learning.
WHAT WE OFFER
- Competitive salaries with growth incentives
- Comprehensive health, dental, and vision insurance
- \#AnywhereAugust \- We support remote work experiences to expand perspectives and personal growth
- 15 Vacation Days, 5 Flex Days, 5 Sick Days, and remote work options year\-round
- Fully paid parental leave for both parents
- Summer Fridays from Memorial Day to Labor Day
- Winter Recess between Christmas and New Years
- Commuter and Parking Benefits through Wage Works
- Eligible to contribute to your 401(K) Retirement Plan after six (6\) months of employment.
- Employee Assistance Programs to support your day to day
LOCATION
Remote (United States)
COMPENSATION
- Comp Range: $165K \- $200K \+ Bonus
EQUAL OPPORTUNITY AND ACCESSIBLE WORKPLACE
Recruitics is an equal\-opportunity employer. We value a culture of inclusion and diversity within our workforce and are committed to maintaining a workplace free from prohibited employment conduct, including discrimination or harassment based on race, color, national origin, sex, age, religion, disability, genetic information, sexual orientation, gender identity or expression, marital status, domestic partner status, civil partnership, status as a covered veteran, status in the Uniformed Services of the United States, citizenship and any other characteristic protected by State and Federal law.
We are committed to creating an inclusive and accessible process for all individuals. If you require any accommodations during the application or interview process due to a disability, please let us know. We will work with you to ensure your needs are met in a timely and respectful manner.
Applicants must be at least 18 years old to apply.
At Recruitics, protecting our talent community—candidates, clients, and partners—is always a top priority. As you navigate your application journey, please keep these tips in mind to stay safe:
- We'll never ask you to pay for anything—that includes applications, interviews, background checks, or equipment.
- We only communicate via official email addresses ending in @recruitics.com—never from Gmail or suspicious lookalikes.
- We do not request personal financial information such as bank details, credit card numbers, or wire transfers.
- If something feels off or doesn’t seem right, pause and contact us directly at careers@recruitics.com.
We’re thrilled you’re interested in working with us. Just make sure it’s really us you’re talking to. Stay sharp—and stay safe.
THIS POSITION IS FOR A CURRENT VACANCY, AND THE EMPLOYER INTENDS TO FILL THIS POSITION BY SEPTEMBER 16, 2026\.
Pay: $165,000\.00 \- $200,000\.00 per year
Benefits:
- 401(k)
- Dental insurance
- Employee assistance program
- Health insurance
- Paid time off
- Parental leave
- Vision insurance
Application Question(s):
- Will you now or in the future require sponsorship for employment visa status (e.g., H\-1B, O\-1, TN, etc.)?
Work Location: Remote
Salary Context
This $165K-$200K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Recruitics, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($182K) sits 17% below the category median. Disclosed range: $165K to $200K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Recruitics AI Hiring
Recruitics has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $200K - $200K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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