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About This Role
Our Purpose
At SentinelOne, we are driven by a clear purpose: to give the advantage to those who secure our future. As AI reshapes how organizations build, operate, and innovate, the responsibility to protect them becomes more critical than ever. When you join SentinelOne, your work helps protect global enterprises, critical infrastructure, and the technologies shaping tomorrow. If you are motivated by meaningful challenges and want your impact to be real, measurable, and global, you will find purpose here.
About Us SentinelOne is a company at the intersection of AI and security, pioneering a new operating model for cybersecurity. Our AI-native platform unifies protection across endpoint, cloud, identity, data, and AI systems to deliver autonomous detection and response with clarity and speed. By combining real-time analytics, intelligent automation, and a unified data foundation, we reduce noise, simplify complexity, and empower security teams to focus on what truly matters.
Our teams are builders, problem-solvers, and innovators committed to shaping the future of security. If you are excited to solve hard problems alongside talented, mission-driven people, we invite you to help us build a safer future for humanity.
What Are We Looking For? We’re looking for people who are relentlessly curious and committed to continuous learning. AI is reshaping every function across our business, and we enable every team member, regardless of role or level, to build fluency in AI tools and concepts. Those who thrive here actively seek out new solutions, experiment thoughtfully, and apply what they learn to drive better, faster, smarter outcomes.
As a Director, Marketing AI & GTM Automation, you will be tasked with building and leading a marketing-led team that owns how AI and automation are applied across the go-to-market motion. you will set the AI strategy for marketing's GTM work, decide which use cases get built and in what order, ship the systems that transform how teams execute, and run those systems as durable, measured infrastructure. this role owns the full arc of strategy, implementation, and run-the-business work, partnering daily with demand generation, PLG, SDR, product marketing, brand, and marketing operations, as well as revenue operations, revenue technology, sales, IT/security, and legal..
What Will You Do? Primary responsibilities include:
- AI use-case roadmap: Maintain a prioritized, revenue-linked backlog of AI and automation use cases across the GTM funnel. Translate operational friction ("our lead response is too slow," "campaign QA is manual") into scoped, sequenced initiatives with defined success metrics, rather than chasing tool novelty.
- Governance and trust: Own the marketing-specific guardrails for AI, including what systems are permitted to do on the brand's behalf, what data they can access, which outputs require human review, and how everything is logged and audited. Coordinate with legal, security, and compliance on policy and risk.
- The marketing technology stack and its AI layer: Own the architecture, integration layer, and roadmap for the marketing tech stack (marketing automation, CDP, orchestration, analytics, and the marketing side of CRM) and the AI layer that now sits across it. Partner with revenue technology and sales-systems owners where workflows cross team lines, rather than absorbing their scope. Manage vendor selection, contracts, and consolidation; prevent tool sprawl.
- Implementation and delivery: Set standards for how AI agents and agentic workflows are designed, built, evaluated, and deployed. Own the evaluation discipline, including output quality scoring, benchmarking, and regression testing, so that "it works" is measured, not asserted.
- Enablement and adoption: Build the skill libraries, prompt libraries, training, and coaching motion that get the broader marketing org self-sufficient. Adoption is a first-class deliverable, not an afterthought.
- Team and craft: Hire, develop, and lead a small team of specialists (GTM engineers, a marketing technology/ops lead, an enablement/governance lead). Set the technical bar, run the operating cadence, and create the career paths that keep scarce talent.
- Measurement: Define and report the metrics that prove the function's value, including cycle-time reduction, output quality, cost-to-serve, and pipeline and revenue influence, to the CMO and cross-functional leadership.
What Skills and Knowledge Will You Bring? Ideal candidates will have:
- 8+ years across marketing/GTM operations, marketing technology, growth, or a related function, with meaningful time owning systems that directly touched pipeline or revenue
- 3+ years leading and developing people, ideally a mix of technical and operational talent
- Demonstrated ownership of a modern GTM/marketing tech stack, including Salesforce, Marketo, CDP platforms (Segment, Tealium, mParticle, Adobe Real-Time CDP), and analytics/orchestration tools (Snowflake, Looker, Tableau, Adobe Experience Platform)
- Hands-on fluency with how AI is built into GTM work today, including agent frameworks (LangChain, CrewAI, LlamaIndex), LLM platforms (Claude, OpenAI, Gemini), evaluation/observability tools (LangSmith, Braintrust), and human-in-the-loop review and prompt architecture practices
- A track record of translating strategy into shipped systems, and of driving adoption across teams you don't directly manage
- Strong cross-functional judgment, comfortable partnering with legal, security, sales, and product to ship safely
- Preferred: Experience standing up or scaling an AI/automation function inside a larger marketing or broader GTM organization
- Preferred: Familiarity with agent frameworks, orchestration platforms (e.g., n8n, Make), APIs/MCP integrations, and lightweight scripting (SQL, Python, or JavaScript), enough to be dangerous, not necessarily to build production code yourself
- Preferred: B2B / enterprise SaaS background
- Preferred: Experience building measurement and governance frameworks for AI in a regulated or brand-sensitive environment
Why SentinelOne? AI is redefining how the world operates and rewriting the rules of security in real time, and SentinelOne was built for this moment. From day one, we architected an AI-native platform designed to operate at machine speed, not as an add-on to legacy systems but as the foundation itself. If you want to build where innovation and impact move together, this is that place.
We invest in our Sentinels with comprehensive, competitive benefits designed to support you and your family:
Equity & Rewards
- Restricted Stock Units (RSUs)
- Employee Stock Purchase Plan (ESPP)
Time Off & Wellbeing
- Flexible time off
- Paid company holidays and paid sick time
- Gender-neutral parental leave
- Grandparent leave
Insurance & Financial Security
- Medical, dental, and vision coverage
- 401(k) retirement plan with company match
- Life and disability insurance
- Health and dependent care FSA
- Voluntary benefits (hospital, accident, critical illness)
- Employee Assistance Program (EAP)
- ARAG pre-paid legal
- Nationwide pet insurance
- Cancer Care program
- Global business travel medical insurance
Work Perks & Flexibility
- Home office allowance
- Mobile phone reimbursement
Wellness & Lifestyle
- Wellness coach
- Wellness/gym reimbursement
- Fertility coverage
- Adoption & surrogacy reimbursement
This U.S. role has a base pay range that will vary based on the location of the candidate. For some locations, a different pay range may apply. If so, this range will be provided to you during the recruiting process. You can also reach out to the recruiter with any questions.
Base Salary Range
$180,000—$270,000 USD
SentinelOne is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.
SentinelOne participates in the E-Verify Program for all U.S. based roles.
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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At SentinelOne, 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 $212,040 based on 13,780 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
SentinelOne AI Hiring
SentinelOne has 3 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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