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About This Role
Location
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Remote U.S.
Employment Type
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Full time
Location Type
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Remote
Department
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People
Compensation
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- Cash Compensation $140K – $175K • Offers Equity
At Vanta, our mission is to help businesses earn and prove trust.We believe that security should be monitored and verified continuously, and we empower companies to practice better security and prove it with ease. Vanta has a kind and talented team, and while some have prior security experience, many have been successful at Vanta without it.
As the People Automation \& AI Partner at Vanta, you'll be the hands\-on builder embedded in our Recruiting and People teams, designing, shipping, and scaling the workflows, agents, and automations that remove manual work across recruiting and People operations so our team can focus on the work that moves the business.
Recruiting is the front door to that talent, and the speed, quality, and consistency of our hiring engine is a top priority for this role. The People team at Vanta is building the infrastructure to hire, develop, and retain world\-class talent at scale. We're not just running processes, we're rethinking them. That means bringing in the tooling, AI, and automation capabilities to match the speed and ambition of the company we're building.
What you’ll do as a People Automation \& AI Partner at Vanta:
- Design, build, and maintain AI\-powered workflows, automations, apps, and agents (Claude, Claude Code, Dust, Zapier) that eliminate manual lift across Recruiting and People operations
- Own Dust agent architecture for the People team: build and maintain agents, skills, and connectors that support recruiters, coordinators, hiring managers, and People partners across pods
- Take team\-generated concepts and one\-off ideas and turn them into durable, documented, adopted production workflows
- Build and maintain automation across the recruiting funnel, sourcing outreach, interview scheduling and coordination, candidate communication, and offer\-to\-hire handoffs, as a first\-class use case for this role
- Serve as a technical build partner for Ashby\-driven workflows, treating the ATS as a primary automation surface alongside Dust and Claude
- Proactively identify automation opportunities across People processes and prototype solutions fast, from AI\-powered reporting pipelines to candidate communication workflows
- Partner with People Systems on automations that touch system architecture or data structure, without owning that configuration yourself
- Partner with People Operations on workflow automations that support their process execution
- Partner with broader Recruiting \& People Operations teams to structure and maintain the knowledge base (Confluence/Guru) that powers AI agents and automations
- Evaluate, pilot, and implement new AI and workflow tools as the market evolves, with a bias toward tools that meaningfully reduce manual effort for the team
- Establish clear operating patterns for how automations and agents are launched, documented, and maintained over time
- Contribute to team AI fluency by sharing what you're building and what's working
- Build lightweight technical solutions using workflow builders, scripts, and integrations across our recruiting and People tool stack (Ashby, TeamOhana, Juicebox, LinkedIn, Zapier, Checkr, Jira, HiBob, Workday)
How to be successful in this role:
- Hands\-on experience building AI workflows, automations, or agents in a business operations context, you've shipped things, not just prompted things
- Experience with Claude Code or similar AI builder/operator environments, beyond AI\-assisted writing or research
- Proven ability to translate messy, manual business processes into scalable, adopted workflows or internal tools
- Technical builder instincts in an operations environment; comfortable shipping without a full engineering team behind you
- Experience working inside Recruiting/Talent, People, HR, or Recruiting functions with non\-technical stakeholders
- Familiarity with ATS\-driven workflows (Ashby or similar) is a strong plus
- Sound judgment on when AI is the right tool versus standard automation, scripts, or process redesign
- Track record of driving adoption of what you build — not just shipping it, but ensuring it sticks
- Comfortable with ambiguity; this role shapes its own playbook and creates new patterns as the function evolves
- Analytical or data science background is a plus
- Open to using AI to amplify their skills and strengthen their work \- demonstrating curiosity, a willingness to learn, and sound judgment in applying AI responsibly to improve efficiency and impact.
- Must be authorized to work in the U.S. without the need for current or future employer sponsorship.
What you can expect as a Vanta’n:
- Industry\-competitive salary and equity
- Comprehensive medical, dental, and vision coverage, with 100% of employee\-only benefit premiums covered for most medical plans
- 16 weeks paid Parental Leave for all new parents
- Health \& wellness stipend
- Remote workspace, internet, and cellphone stipend
- Commuter benefits for team members who report to the SF and NYC office
- Family planning benefits
- Matching 401(k) contribution with immediate vesting
- Flexible PTO policy, plus 80 hours of Sick Time
- 11 company\-paid holidays
- Virtual team building activities, lunch and learns, and other company\-wide events!
- Offices in SF, NYC, London, Dublin, Tel Aviv, and Sydney
To provide greater transparency to candidates, we share base pay ranges for all US\-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar\-stage growth companies. Final offer amounts are determined by multiple factors and may vary based on candidate location, skills, depth of work experience, and relevant licenses/credentials.
\#LI\-remote
*At Vanta, we are committed to hiring diverse talent of different backgrounds and as such, it is important to us to provide an inclusive work environment for all. We do not discriminate on the basis of race, gender identity, age, religion, sexual orientation, veteran or disability status, or any other protected class. As an equal opportunity employer, we encourage and welcome people of all backgrounds to apply.*
About Vanta
We started in 2018, in the wake of several high\-profile data breaches. Online security was only becoming more important, but we knew firsthand how hard it could be for fast\-growing companies to invest the time and manpower it takes to build a solid security foundation. Vanta was inspired by a vision to restore trust in internet businesses by enabling companies to improve and prove their security. From our early days automating security monitoring for compliance standards like SOC 2, HIPAA and ISO 27001 to creating the world's leading Trust Management Platform, our vision remains unchanged.
Now more than ever, making security continuous—not just a point\-in\-time check— is essential. Thousands of companies rely on Vanta to build, maintain and demonstrate their trust— all in a way that's real\-time and transparent.
Referral Instructions
If you are being referred for the role, please contact that person to apply on your behalf.
Compensation Range: $140K \- $175K
Salary Context
This $140K-$175K range is below 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 Vanta, 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($157K) sits 28% below the category median. Disclosed range: $140K to $175K.
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.
Vanta AI Hiring
Vanta has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $175K - $175K.
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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