Interested in this AI/ML Engineer role at OneTrust?
Apply Now →Skills & Technologies
About This Role
### Strength in Trust
OneTrust's mission is to enable innovation through the responsible use of data and AI. We believe that ensuring data is trusted shouldn't slow teams down—it should accelerate what's possible. This led us to develop the first technology platform for responsible data use in 2016\. Today, with AI representing the latest and most impactful expansion of data yet, OneTrust is once again redefining what responsible innovation looks like. OneTrust, the AI‑Ready Governance Platform™, unifies regulatory intelligence, automation, and connected governance workflows so businesses can continue to move at the speed of AI while ensuring good governance to prevent data misuse at scale. Trusted by thousands of organizations worldwide, OneTrust is shaping the future where trusted data becomes a transformative force for business and society.
The Challenge
-----------------
OneTrust is seeking a talented Product Marketing Manager to support product marketing for the AI Governance solution. As a Product Marketing Manager, you will serve as a key liaison between the product management, marketing, and sales organizations and our customers. You will work cross\-functionally and leverage both technical and storytelling skills to drive messaging, create content, and launch products to enable sales teams and help drive product adoption. These efforts will be guided by the go\-to\-market strategy for AI Governance, which you will help shape and execute in partnership with the Director of Product Marketing and cross\-functional stakeholders.
Your Mission
----------------
### Voice of the Customer
- OneTrust offers a comprehensive AI Governance solution designed to help organizations govern, operationalize, and scale responsible AI across their ecosystem. Your mission is to deeply understand the ICP and buyer personas for AI Governance, including their business challenges, goals, motivators, and market trends.
- Develop an informed point of view on our AI Governance solution and synthesize customer, field, and market feedback to help inform messaging, launches, and product direction.
### Messaging \& Positioning
- Articulate the product and capability value proposition clearly and convincingly, highlighting what will deliver the most impact and value for your buyer personas. Ensure buy\-in from stakeholders across the organization.
- Help communicate AI Governance within OneTrust's broader platform while communicating OneTrust's unique perspective on AI Governance to internal and external audiences.
- Create compelling customer materials, including pitch decks, product datasheets, case studies, battlecards, and training resources.
### Go\-to\-Market Strategy \& Execution
- Partner on a GTM strategy for AI Governance product launches and release moments that will improve product adoption and growth, while continuously refining ICP, use\-case fit, and market readiness.
- Execute that strategy by driving cross\-functional alignment to support successful product launches.
- Work closely with Sales to ensure they are equipped with the latest product knowledge and sales guidance. Enable them to act as trusted advisors to our prospects and customers, ensuring they can clearly articulate how our AI Governance solution addresses customer pain points and adds business value.
### Market and Competitive Analysis
- Work with the Competitive team to conduct thorough market research and competitive analysis to inform product and marketing strategies. Regularly update the team on key industry trends, competitor moves, and market opportunities.
- Support analyst relations for AI Governance, including briefing prep, survey inputs, narrative development, follow\-up materials, and other solution\-level assets that help scale analyst engagement.
### Demand Generation Support
- Work closely with the demand generation and content teams to develop and execute demand generation campaigns that drive awareness and lead generation.
### Customer Advocacy
- Foster strong relationships with key customers to gather insights, develop case studies, support advisory board efforts, and create advocates in the industry.
### Leadership and Influence
- Given the cross\-functional nature of the role, must possess strong collaboration skills to foster alignment and orchestrate efforts across diverse teams — product, marketing, sales, customer success, and more.
- Moreover, the role must be capable of influencing without authority, driving clarity and momentum across multiple stakeholders and initiatives.
You Are
-----------
- BA/BS degree in marketing, business, or engineering equivalent
- 5\+ years of product marketing experience at a SaaS or other high\-tech company
- Highly collaborative with a deep sense of ownership and accountability
- Experience with stakeholder management, influencing and managing multiple stakeholders internally
- Demonstrated customer\-centric mindset with empathy for users and buyers, translating their needs into effective messaging and go\-to\-market plans
- Proficient in data\-driven decision\-making and analytics
- Strong technical competence with a solid understanding of the domain and product design
- Exceptional communication and presentation skills, articulating complex technical concepts to diverse audiences
- Comfort supporting competitive intelligence and analyst relations for a product or solution area
- Comfort using AI tools to accelerate research, content development, and workflow execution, with good judgment around oversight and governance
- Experience marketing AI, governance, risk, compliance, security, or data governance solutions, with a strong understanding of how AI is transforming B2B organizations. Knowledge of or experience with AI Governance solutions is a plus.
### Where we Work
We are embracing an office\-first culture, encouraging three days a week in office for most roles, with meaningful opportunities to collaborate and celebrate in person.
Each role may have specific requirements or flexibility depending on the scope of the position, so we encourage you to verify this with your recruiter during your first interview.
### Benefits
As an employee at OneTrust, you will be part of the OneTeam. That means you'll receive support physically, mentally, and emotionally so that you can do your best work both in and out of the office. This includes comprehensive healthcare coverage, flexible PTO, equity RSUs, annual performance bonus opportunities, retirement account support, 14\+ weeks of paid parental leave, career development opportunities, company\-paid privacy certification exam fees, and much more. Specific benefits differ by country. For more information, talk to your recruiter or visit onetrust.com/careers.
### Resources
Check out the following to learn more about OneTrust and its people:
- OneTrust Careers on YouTube
- @LifeatOneTrust on Instagram
### Your Data
You have the right to have your personal data updated or removed. You also have the right to have a copy of the information OneTrust holds about you. Further details about these rights are available on the website in our Privacy Overview. You can change your mind at any time and have your personal data removed from our database. In order to do this you must contact us and let us know you wish to be removed. The request should be made on the Data Subject Request Form.
Recruitment fraud warning: OneTrust is aware of scams involving false offers of employment with our company. The fraudulent jobs, interviews and job offers use fake websites, email addresses, group chat and text messages. Be aware that we never ask candidates for personal information, IDs or bank information during the interview process. We do not interview prospective candidates via instant message or group chat, and do not require candidates to purchase products or services, or process payments on our behalf as a condition of any employment offer. Please note that any legitimate interview availability requests will come directly from a OneTrust recruiter with an "@onetrust.com" email address. You may also receive legitimate emails from "@us.greenhouse\-mail.io". Recruiters will only reach out to candidates who have applied for a role through our ATS (Greenhouse) or prospects via LinkedIn InMail. Job offers will come from a recruiter and may have a "@docusign.net" email address. For more information or if you have been targeted please reach out to [email protected].
### Our Commitment to You
When you join OneTrust you are stepping onto a launching pad — the countdown has begun. The destination? A career without boundaries working alongside a diverse and inclusive crew who is passionate about doing meaningful work. As a pioneer, your voice and expertise will help chart the direction of an entirely new category. Our commitment to putting people first starts with you. Your growth is part of the mission. Our goal is to give you the power to embark on the next phase of your uniquely, unique career.
OneTrust provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by local laws.
Salary Context
This $127K-$191K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At OneTrust, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($159K) sits 26% below the category median. Disclosed range: $127K to $191K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
OneTrust AI Hiring
OneTrust has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US. Compensation range: $191K - $247K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.