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About This Role
At ExtraHop, we're on a mission to protect and empower the connected enterprise. We reveal what is happening in the very infrastructure that sustains businesses, lives, and communities, and ensure the integrity of networks, data, systems, and processes. Organizations rely on ExtraHop to provide visibility into the cyber threats, vulnerabilities, and network performance issues that evade their existing security and IT tools. With this insight, organizations can investigate smarter, stop threats faster, and keep operations running.
Our mission is fueled by a profound social and moral responsibility to be the best at what we do, ensuring a secure world where everyone can thrive. If this sounds like a place you'd like to spend the next chapter of your career, we'd love to hear from you.
Position Summary
At ExtraHop we provide best\-in\-class network visibility and security to industries ranging from healthcare to payment processing. We don't just solve problems for our users, we help them rise above the noise of threats, alerts, and corporate gridlock. Our products enable organizations to harness the power of their wire data and achieve true security and exceptional performance.
The Machine Learning Infrastructure team owns the tools that empower the development of new machine learning models and data intelligence at Extrahop. Scalability, efficiency and experimentation is the lifeblood of machine learning. Our goal on the Machine Learning Infrastructure team is to make it fast, easy, and cost effective to design, code, deploy, perform experiments, and to develop and support the production environment to deliver cybersecurity detections in a timely manner.
As an Engineering Manager of this team, you will drive and develop the team on design and build infrastructure and tooling to support ongoing research and scalability efforts. You will attract, develop and cultivate a great engineering team and environment to maintain our technical leadership in the industry.
You will guide the development team on responsibilities that include shepherding day\-to\-day design decisions, product roadmap progress, and long\-term architectural and technology choices while encouraging healthy work\-life balance. You listen to your employees and value their opinions. You believe that great things happen when engineering teams are empowered, have clear goals and a leader that removes roadblocks.
Key Responsibilities
Work as a product owner to deliver on the product vision and feature priorities. You should have a strong track record of making tough tradeoffs to balance scope, quality, supportability, performance, and time criticality.
- Guide the team through design/implementation for complex technical projects.
- Work closely with the internal stakeholders to ensure the product meets quality/stability requirements of enterprise customers, leveraging experience inventing and improving technology of performance/stability/scale.
- Manage end to end product ownership, from planning and design to on call product support. Manage/fix/communicate issues that arise during escalations/customer issues.
Required Qualifications
- BS degree or equivalent in CS or a related Engineering Field
- 7\+ years experience in software development and 3\+ years in team lead/management role
- Managed teams that have delivered machine learning cloud infrastructure and features through design, architecture, and development
- Experience in cloud infrastructure as well as infrastructure as code, database design or SQL query performance optimization
- Familiarity with machine learning models, such as LLMs, Clustering and Anomaly Detection
- Solid understanding of DevOps practices, CI/CD pipelines, and strategies for achieving scalability and availability.
- Basic understanding of threat detection, intrusion prevention, and incident response strategies.
- Experience in software development life cycle using agile methodologies
- Excellent organizational and interpersonal skills
- Basic understanding of threat detection, intrusion prevention, and incident response strategies.
The salary range for this role is $170,000 \- $195,000 \+ bonus \+ benefits
ABOUT EXTRAHOP
ExtraHop is reinventing Network Detection and Response (NDR) to offer enterprises unparalleled visibility, context, and control against emerging threats. The platform integrates NDR with Network Performance Management (NPM), Intrusion Detection Systems (IDS), and forensics, providing a single, comprehensive solution. By decrypting and analyzing complete packet\-level data at wire speed and leveraging cloud\-scale machine learning, ExtraHop empowers Security Operations Centers (SOCs) to detect, investigate, and remediate modern cyber risks in real time across their entire hybrid infrastructure, including data center, cloud, and SASE environments.
This comprehensive approach and market innovation have earned ExtraHop unique recognition as the only NDR vendor acknowledged as a leader by all major analyst firms, including the 2025 Gartner® Magic Quadrant for Network Detection and Response™, the 2025 Forrester® Wave for Network Analysis and Visibility, the 2024 IDC® Marketscape for NDR, and the 2025 Gigamon® Radar Report for Network Detection and Response. Since 2007, ExtraHop has consistently helped organizations worldwide extract in\-depth network telemetry and contextual insights, affirming its commitment to protecting and empowering the connected enterprise.
OUR VALUES
Our culture is rooted in our five Values. These set the expectations for how we work individually and collectively as a team.
Lead with Purpose: We are driven to deliver results that create a positive impact for our customers, partners, and colleagues.
Act with Integrity: We operate with transparency, authenticity, and always in the best interest of the company.
Find a Way: We are resourceful, tackle hard problems with a sense of urgency and ownership, and do what it takes to get the job done.
Innovate: We listen to customers, partners, and the market, and respectfully push boundaries and challenge the status quo.
Share Success: We run together, we win together. We value diverse perspectives, hold space for all voices, and achieve the best results as a team.
BENEFITS
Employees' wellbeing is top of mind for the ExtraHop team. Employees and their families will have the option to participate in the following benefits:
- Health, Dental, and Vision Benefits
- Flexible PTO, Sick Time Prorated Based on Date of Hire, and All Federal Holidays (US Only) \+ 3 Days of Paid Volunteer Time
- Non\-Commissioned Positions may be eligible to participate in the Annual Discretionary Bonus Plan
- FSA and Dependent Care Accounts \+ EAP, where applicable
- Educational Reimbursement
- 401k with Employer Match or Pension where applicable
- Pet Insurance (US Only)
- Parental Leave (US Only)
- Hybrid and Remote Work Model
Our people are our most important competitive advantage, leading the charge against cyber criminals. Join the fight today!
To learn more, visit our website or follow us on LinkedIn.
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Salary Context
This $170K-$195K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At ExtraHop Networks, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. Disclosed range: $170K to $195K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
ExtraHop Networks AI Hiring
ExtraHop Networks has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Seattle, WA, US. Compensation range: $195K - $195K.
Location Context
AI roles in Seattle pay a median of $227,400 across 1,084 tracked positions. That's 14% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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