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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
ExtraHop is seeking a Senior Engineering Manager to lead the Applied Machine Learning team responsible for behavioral detections within the ExtraHop Network Detection and Response (NDR) platform.
This team develops machine learning systems that analyze large\-scale network telemetry and surface meaningful behavioral signals for Security Operations Center (SOC) analysts. The work sits at the intersection of applied machine learning, cybersecurity, and high\-volume time\-series data.
This role owns the applied machine learning strategy for behavioral detection within the product. You will lead a team responsible for designing, evaluating, and operationalizing models that identify anomalous or suspicious patterns in complex network activity. The position combines technical leadership, scientific rigor, and product influence to ensure machine learning capabilities translate directly into actionable security insights.
Key Responsibilities
- Lead and grow a multidisciplinary team of data scientists and software engineers building production machine learning models and supporting systems for behavioral detection.
- Drive the research, development, evaluation, and operational monitoring of models that analyze large\-scale network telemetry, including time\-series and behavioral anomaly detection.
- Establish high standards for experimental rigor across the team, including statistically sound experimentation, clear evaluation methodologies, and disciplined model validation.
- Own the technical direction for production ML systems supporting behavioral detections, including experimentation frameworks, model lifecycle management, data pipelines, and monitoring.
- Collaborate closely with Product Management and Security Research to translate machine learning capabilities into practical detection signals that improve SOC analyst workflows.
- Influence the product roadmap by identifying opportunities where applied machine learning can materially improve detection quality and analyst productivity.
- Mentor senior data scientists and engineers while fostering a culture of scientific rigor, intellectual curiosity, and technical ownership.
- Represent the machine learning function in cross\-organizational discussions and communicate technical strategy and outcomes to senior leadership.
- Stay current with advances in machine learning research and engineering practices and guide the team in adopting techniques that meaningfully improve detection performance.
Required Qualifications
- Bachelor's degree or equivalent experience in Computer Science, Statistics, Machine Learning, or a related quantitative field; advanced degree preferred.
- 5\+ years experience leading applied machine learning or machine learning engineering teams delivering production systems.
- Strong background in machine learning, statistics, or a related quantitative discipline.
- Experience guiding experimental design, model evaluation strategies, and statistically rigorous decision making.
- Experience building or operating production ML systems, including model lifecycle management, data pipelines, and monitoring.
- Experience working with large\-scale telemetry, time\-series data, or behavioral modeling problems.
- Demonstrated ability to partner with product and domain experts to translate machine learning capabilities into user\-facing value.
- Strong technical judgment and the ability to guide architecture and modeling decisions.
- Experience mentoring senior individual contributors and building high\-performing ML teams.
- Exceptional communication skills; able to translate model performance, technical tradeoffs, and data science concepts for product, executive, and cross\-functional audiences.
- Consistent, reliable, and accountable in attendance and execution.
Preferred Qualifications
- Experience with network security, NDR, or related security domains; familiarity with tools and frameworks commonly used in threat detection.
- Experience building ML systems on AWS cloud infrastructure, including data pipelines and model deployment at scale.
- Familiarity with compliance requirements such as FedRAMP or NIST SP 800\-53 and their implications for data science workloads.
- Experience with containerization technologies (Docker, Kubernetes) for ML workload deployment.
- AWS certification such as AWS Certified Solutions Architect or Machine Learning Specialty.
- Understanding of threat detection, intrusion prevention, and incident response strategies.
The salary range for this role is $200,000 \- $218,000 \+ 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 $200K-$218K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($209K) sits 25% above the category median. Disclosed range: $200K to $218K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
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: $218K - $218K.
Location Context
AI roles in Seattle pay a median of $223,600 across 678 tracked positions. That's 22% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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