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About This Role
At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI\-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high\-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low\-ego individuals who thrive in dynamic and fast\-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.
As we continue to scale globally, we are investing in security capabilities that help us better understand, anticipate, and mitigate threats targeting Snowflake, our customers, and our ecosystem. We are looking for a Principal Security Engineer \- Threat Intelligence who will help shape the next phase of Snowflake’s Threat Intelligence program and extend the reach and impact of Threat Intelligence across Snowflake. This role will combine deep intelligence expertise with strong engineering and program leadership skills, with AI and automation as core primitives in how we collect, analyze, prioritize, and operationalize intelligence.
The ideal candidate will help Snowflake leadership and security stakeholders make informed, risk\-based, and data\-driven decisions based on actionable threat intelligence. You will identify and track threat actors targeting cloud\-native environments such as Snowflake, translate intelligence into concrete defensive outcomes, and build scalable approaches that improve how intelligence is delivered across the company.
This is a principal\-level individual contributor role for someone who can operate strategically and technically: driving program maturity, building durable partnerships across Security and Engineering, and engineering AI\-assisted workflows that help us move faster without sacrificing quality.
WHAT YOU NEED:
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- Deep experience in threat intelligence, with strong background in several of: adversary intelligence, intrusion intelligence, supply\-chain intelligence, identity intelligence, domain intelligence, and threat\-informed defense.
- Strong understanding of today’s threat actor ecosystem, including nation\-state actors, criminal organizations, ransomware groups, fraud ecosystems, and the platforms and communities that enable them.
- Demonstrated ability to operationalize threat intelligence and influence security priorities in partnership with detection, incident response, product security, cloud security, anti\-abuse, and other stakeholders.
- Strong engineering skills, including experience writing code in high\-level languages such as Python or Go, building automations, and working with data\-heavy security workflows.
- Experience building or driving AI\-assisted workflows for intelligence analysis, research triage, summarization, collection, prioritization, or investigative support, and good judgment about where AI adds value versus where human analysis is required.
- Ability to research threat actors’ TTPs, infrastructure, targets, and objectives, and map those risks to Snowflake’s product, enterprise, and customer environment.
- Experience with OSINT tools, data sources, investigative methodologies, and intelligence reporting for technical and executive audiences.
- Strong understanding of threat hunting and threat detection methodologies, and the ability to turn intelligence into hunts, detection opportunities, and control recommendations.
- A risk\-based approach to security, with the ability to prioritize work based on business impact and evolving threat conditions.
- A humble, team\-oriented mindset with a bias toward collaboration, execution, and raising the bar for the broader team.
WHAT YOU WILL DO:
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- Help define and mature the strategy for Threat Intelligence at Snowflake, including where the program should invest in people, processes, engineering, and AI\-enabled capabilities.
- Identify, profile, and track threat actors targeting Snowflake, our customers, partners, and ecosystem, and translate that intelligence into relevant, actionable outcomes.
- Operationalize threat intelligence to help prioritize security initiatives and drive action with the relevant security teams and stakeholders.
- Produce high\-quality intelligence reports, assessments, briefs, and leadership\-ready communications based on external events, internal requirements, and proactive research.
- Engineer solutions that improve the efficiency, scale, and impact of the Threat Intelligence program, including automations, collection pipelines, enrichment workflows, and analyst tooling.
- Build and improve AI\-assisted intelligence workflows for tasks such as report triage, signal enrichment, summarization, vendor/customer monitoring, and threat\-informed hunts, with strong measurement and quality..
- Partner closely with Threat Detection, Incident Response, and other security teams to convert intelligence into detections, threat hunts, investigative pivots, and control recommendations.
- Monitor alerts, intelligence feeds, vendor reporting, and external developments for threat events that may affect Snowflake.
- Drive standards for how intelligence is curated, evaluated, delivered, and measured so the program remains high\-signal, timely, and scalable.
- Mentor other engineers and analysts by raising the team’s technical depth, analytic rigor, and operational maturity.
MINIMUM QUALIFICATIONS:
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- Significant experience in threat intelligence, cyber threat research, intelligence engineering, or closely related security disciplines.
- Experience researching and tracking sophisticated threat actors targeting cloud\-native and SaaS environments.
- Experience writing code in a high\-level programming language such as Python or Go and using code to automate manual workflows or analyze security data at scale.
- Experience handling data programmatically using tools such as SQL and Python, ideally against large datasets relevant to security analytics or intelligence workflows.
- Experience collaborating across multiple security functions and communicating effectively with technical stakeholders and leadership.
- Strong understanding of enterprise security controls, threat hunting, and detection methodologies.
- Experience with one or more major cloud providers (AWS, Azure, GCP) and familiarity with the risks that impact cloud and SaaS environments.
PREFERRED QUALIFICATIONS:
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- Experience leading or materially shaping a Threat Intelligence program at scale.
- Experience building AI/ML\-assisted security workflows or evaluating AI systems for security use cases.
- Experience with data engineering, workflow orchestration, or production\-grade systems that support intelligence or security operations at scale.
- Experience with Snowflake or equivalent cloud data platforms for large\-scale analysis and investigative workflows.
- Experience presenting externally, publishing research, or demonstrating thought leadership in the security space.
- Experience building capabilities that support intelligence\-driven detection, hunting, or response at a global scale.
WHY YOU SHOULD WORK WITH US:
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- We are laser focused on doing security better, and we do not tolerate the status quo.
- We have strong demand from our customers and strong support from the business for security, giving us meaningful runway to build next\-generation capabilities.
- We are a great team with a diverse set of backgrounds and skills, and we care deeply about impact, collaboration, and execution.
- You will help solve security problems at global scale, leveraging Snowflake’s platform and modern AI capabilities to raise the bar for defenders.
- The opportunity for impact on Snowflake, our customers, and the broader security ecosystem is enormous.
ABOUT THE THREAT INTELLIGENCE TEAM:
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The Threat Intelligence team at Snowflake operates with a vision of proactively detecting threats based on risk and data\-driven decisions. Our mission is to proactively identify relevant threat actors and activity through intelligence, and to translate that intelligence into capabilities and decisions that help Snowflake identify threats early and reduce risk to the business.
*Every Snowflake employee is expected to follow the company’s confidentiality and security standards for handling sensitive data. Snowflake employees must abide by the company’s data security plan as an essential part of their duties. It is every employee’s duty to keep customer information secure and confidential.*
*The application window is expected to be open until June 10, 2026\. This opportunity will remain posted based on business needs, which may be before or after the specified date.*
Snowflake is growing fast, and we’re scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake.
How do you want to make your impact?
For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com
The following represents the expected range of compensation for this role:
- The estimated base salary range for this role is $249,000 \- $357,600\.
- Additionally, this role is eligible to participate in Snowflake’s bonus and equity plan.
The successful candidate’s starting salary will be determined based on permissible, non\-discriminatory factors such as skills, experience, and geographic location. This role is also eligible for a competitive benefits package that includes: medical, dental, vision, life, and disability insurance; 401(k) retirement plan; flexible spending \& health savings account; at least 12 paid holidays; paid time off; parental leave; employee assistance program; and other company benefits.
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Salary Context
This $249K-$357K 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 Snowflake, 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. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($303K) sits 41% above the category median. Disclosed range: $249K to $357K.
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.
Snowflake AI Hiring
Snowflake has 9 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, Data Scientist, AI Product Manager. Positions span CA, US, Menlo Park, CA, US, TN, US. Compensation range: $150K - $379K.
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.
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