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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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Security
Compensation
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- Cash Range $210K – $247K • Offers Equity • This role is also eligible for medical benefits, 401(k) plan, and other company perk programs.
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.
Vanta's Corporate Engineering team is the infrastructure layer that keeps 1,500\+ people connected, secure, and moving fast. AI is now central to how that happens, and this role leads the team that owns the backbone underneath it.
Corporate Engineering owns the shared AI platform for Vanta's internal systems: identity and access for AI tools, cost visibility and controls, sanctioned tooling and guardrails, and the enablement that helps people use those tools well. We do not own product AI, which stays with Engineering, and we do not replace the AI work happening inside individual departments. We build the engineering layer that makes all of it safer, cheaper, and better supported.
The company has moved fast. AI assistant use is widespread across every function, MCP infrastructure is live and self\-serve company\-wide, internal apps ship on a hosted platform, and AI spend has grown to the point where attribution and guardrails genuinely matter. What does not exist yet is a single owner for that platform layer. That is this role.
As Sr. Manager, AI Engineering, you will lead a small, senior team, write the charter for what Corporate Engineering owns versus enables, and build the platform that lets the rest of Vanta adopt AI quickly without accumulating cost, risk, or duplication.
What you’ll do as a Senior Manager, AI Corporate Engineering at Vanta:
- Lead, coach, and grow a senior team spanning platform engineering and technical program management. Set clear direction, hold a high bar, and give the team explicit permission to push back with data.
- Own the AI access layer end to end: identity and authentication for AI tools, connector and integration allowlisting, tiered permissions by function, and guardrails that make adoption safe by default rather than safe by exception.
- Own AI cost engineering. Build attribution down to team and individual, right\-size model selection to actual need, make agent and automation spend traceable, and set budget thresholds and alerting in partnership with Finance.
- Own the MCP layer for internal tools end to end, including commissioning, decommissioning, governance, and the downstream impact assessment that has to happen before a migration ships rather than after it breaks.
- Own the paved path for internal applications: a golden\-path deployment pattern, sensible defaults including private\-by\-default, lifecycle and decommissioning policy so unused apps retire themselves, and a build\-versus\-buy framework that keeps people from rebuilding tools we already own.
- Build the enablement layer that turns adoption into impact: a skills and agent registry that cuts duplication, an evaluation framework for internal AI usage, and technical enablement on sanctioned tools that goes beyond provisioning access.
- Write and hold the charter. Define what Corporate Engineering owns, what it enables, and how work enters the team, then convene the cross\-functional forum that keeps departmental AI efforts coordinated rather than duplicated.
- Partner across Engineering, GRC, Security, Data, Finance, and People. Several of the levers this role depends on sit in those organizations, so the job is to coordinate and influence rather than override.
How to be successful in this role:
- Experience standing up an internal AI or developer platform function rather than inheriting a mature one. You have written the charter, defined intake, and built the roadmap from a blank page.
- The ability to hold scope. You can take a commitment that was made without scoping or resourcing, land a bounded first version, and decline the rest without it reading as abdication.
- Fluency in AI cost engineering: usage attribution, model right\-sizing, token economics, agent cost traceability, and the difference between a hard limit and a useful guardrail.
- An enablement\-first instinct. Your default question is how to make something safe to self\-serve. When you do have to decline access, you always explain why and offer an alternative path to the same visibility.
- Hands\-on technical depth across LLM platform tooling, agent and MCP architecture, identity and OAuth, CI/CD, and infrastructure as code. Enough to review a design honestly and tell the difference between real risk and reported progress.
- Comfort building for an audience that is no longer only engineers. A growing share of the people shipping internal tools are not developers, and the platform has to work for them without lowering the security bar.
- Data governance fluency in a regulated environment. You know what should never enter a prompt, a repository, or a third\-party tool, and you can make that judgment fast without becoming the bottleneck.
- Comfort operating through influence. Product AI, security policy, data platform, and budget authority all sit in partner organizations.
- 8\+ years in platform, infrastructure, or systems engineering, including 3\+ years managing and developing engineers, ideally at a company that scaled through a significant headcount inflection.
- Must be authorized to work in the U.S. without the need for current or future employer sponsorship.
- 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.
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: $210K \- $247K
Salary Context
This $210K-$247K range is above the 75th percentile 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 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 in Demand for This Role
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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($228K) sits 6% above the category median. Disclosed range: $210K to $247K.
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.
Vanta AI Hiring
Vanta has 3 open AI roles right now. They're hiring across AI Product Manager, Data Scientist, AI/ML Engineer. Based in Remote, US. Compensation range: $247K - $286K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 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
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