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About This Role
OneMagnify is a global performance marketing organization working at the intersection of brand marketing, technology, and analytics. The Company’s core offerings accelerate business, amplify real-time results, and help set their clients apart from their competitors. OneMagnify partners with clients to design, implement and manage marketing and brand strategies using analytical and predictive data models that provide valuable customer insights to drive higher levels of sales conversion.
OneMagnify’s commitment to employee growth and development extends far beyond typical approaches. We take great pride in fostering an environment where each of our 700+ colleagues can thrive and achieve their personal best. OneMagnify has been recognized as a Top Workplace, Best Workplace and Cool Workplace in the United States for 10 consecutive years and recently was recognized as a Top Workplace in India.
We’re looking for a Director of Artificial Intelligence to define, lead, and implement innovative AI strategies across all business functions, driving transformative outcomes and measurable results. You will establish strategic priorities for AI initiatives—ranging from foundational research to applied AI solutions—while optimizing resources and driving return on investment. Additionally, you will encourage team growth and expertise, enabling the expansion of AI capabilities across the organization and promoting a culture of innovation and continuous improvement.
About You:
- Transformative leader who drives collaborative, user-centered solutions
- Lifelong learner up-to-date with AI trends and expert at engaging presentations
- Trusted advisor and senior client relationship builder
- Strong expertise in data, artificial intelligence, software engineering, and marketing and media analytics
What you’ll do:
- Develops and Puts into Action AI Strategy: Establishes the organization's AI vision and roadmap, aligning AI initiatives with business goals to foster innovation, efficiency, and measurable value across products, services, and operations.
- Leads AI Research and Development: Manages the creation, development, and deployment of AI models and solutions, including machine learning, natural language processing, computer vision, and generative AI systems.
- Builds and expands AI teams: Leads, mentors, and develops cross-functional engineering teams. Offers suitable supervision and strategic mentorship at every organizational level, from managers to individual contributors.
- Talent Strategy & Development: Partner with Human Resources to build and implement career progression pathways, performance management frameworks, and succession plans to attract, retain, and develop top-tier talent.
- Ensures Ethical and Responsible AI Use: Establishes guidelines and processes to ensure AI solutions are transparent, fair, and aligned with ethical standards, industry regulations, and privacy requirements.
- Partners across OneMagnify to uncover opportunities for AI adoption, refine processes, and deliver solutions that solve complex business challenges.
What you’ll need:
- Over 10 years of experience leading AI and machine learning initiatives. Proven track record in deploying large-scale AI solutions for businesses.
- Experience working across data, analytics, and AI initiatives within Martech, Adtech, customer analytics, personalization, or audience intelligence environments.
- Preferred industry background in B2B, automotive, industrials, or B2B technology, with the ability to translate complex data insights into actionable business strategies.
- Technical Expertise: Extensive knowledge of AI technologies, encompassing machine learning, deep learning, natural language processing (NLP), computer vision, and generative AI frameworks.
- Strategic Leadership: Established skill in formulating and achieving AI strategies that correspond with business aims, produce measurable benefits, and develop transformative impacts. Experience leading cross-functional teams and promoting an innovative culture.
- Strong Business Insight: Proven skill in linking technical strengths to business objectives. Identifies chances for AI use in various sectors. Communicates complex AI ideas clearly to non-technical partners.
- Organizational Leadership: Proven success in structuring and leading multi-tiered teams, including experience managing other managers and technical leads. You have a demonstrated history of attracting top-tier talent, optimizing team structures for scale, and retaining high-performing staff in a competitive market.
- Educational Background: Advanced degree (Master’s or Ph.D.) in Computer Science, Artificial Intelligence, Data Science, or a related field; equivalent experience may be considered. Additional certifications in AI/ML frameworks are a plus. Martech platform experience is also a plus.
Benefits
We offer a comprehensive benefits package including medical, dental, 401(k), paid holidays, vacations, and more.
About us
Whether it’s awareness, advocacy, engagement, or efficacy, we move brands forward with work that connects with audiences and delivers results. Through meaningful analytics, engaging communications and innovative technology solutions, we help clients tackle their most ambitious projects and overcome their biggest challenges.
We are an equal opportunity employer
We believe that Innovative ideas and solutions start with unique perspectives. That’s why we’re committed to providing every employee a workplace that’s free of discrimination and intolerance. We’re proud to be an equal opportunity employer and actively search for like-minded people to join our team.
We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform job functions, and to receive benefits and privileges of employment. Please contact us to request accommodation.
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 3,897 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At OneMagnify, 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 $154,000 based on 8,743 positions with disclosed compensation. Director-level AI roles across all categories have a median of $230,600.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
OneMagnify AI Hiring
OneMagnify has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $160,000 across 1,226 positions. About 16% 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 3,897 open positions tracked in our dataset. By seniority: 111 entry-level, 1,958 mid-level, 1,413 senior, and 415 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (615 positions). The remaining 3,251 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 3,897 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,733), Data Scientist (273), AI Software Engineer (271). 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 (111) are outnumbered by mid-level (1,958) and senior (1,413) 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 415 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (615 positions), with 3,251 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $145,600. 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 (2,064 postings), Aws (1,085 postings), Azure (867 postings), Rag (865 postings), Gcp (697 postings), Pytorch (650 postings), Prompt Engineering (597 postings), Kubernetes (499 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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