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About This Role
Who is Tenable?
Tenable® is the Exposure Management company. Over 40,000 organizations around the globe rely on Tenable to understand and reduce cyber risk. Our global employees support 65 percent of the Fortune 500, 50 percent of the Global 2000, and large government agencies. Come be part of our journey!
What makes Tenable such a great place to work?
Ask a member of our team and they'll answer, "Our people!" We work together to build and innovate best-in-class cybersecurity solutions for our customers; all while creating a culture of belonging, respect, and excellence where we can be our best selves. When you're part of our #OneTenable team, you can expect to partner with some of the most talented and passionate people in the industry, and have the support and resources you need to do work that truly matters. We deliver results that exceed expectations and we win together!
Applicants must be authorized to work for any employer in the U.S. without sponsorship. We are unable to provide sponsorship for work visas of any kind at the time of hire, or at any point during employment. This includes, but is not limited to: F1-OPT, F1-CPT, H-1B, TN, J-1, etc
Your Role:
Tenable's Research Special Operations (RSO) team produces the exposure intelligence and analysis that helps organizations understand and prioritize cyber risk. We translate what's happening in the global threat landscape into structured, actionable intelligence for SOC analysts, vulnerability management engineers, field teams, and CISO-level stakeholders.
We're looking for a Staff Research Engineer who will be primarily responsible for executing the daily intelligence production cycle for our Exposure Intelligence function. You'll run the morning production cycle; sourcing, triaging, classifying, analyzing, and publishing finished intelligence in collaboration with other team members. On a typical day you might assess a newly disclosed zero-day, determine whether it warrants a full analytical workup, write the daily brief that informs operational priorities, and publish an external digest consumed by hundreds of stakeholders, then shift to deeper analytical work, framework development, or external content in the afternoon.
This isn't a traditional security research role. The work blends intelligence analysis, production discipline, and analytical writing on a daily cadence. The right candidate can context-switch between operational triage and deep analysis, writes clearly and concisely to deliver time-sensitive deliverables, and treats repeatable process as a feature rather than a constraint.
Your Opportunity:
Daily Intelligence Production
- You have primary responsibility for executing the operational aspects of the Security Response process. That means monitoring a broad source ecosystem, triaging what matters, and producing the daily operations brief that informs the team's priorities, and an external intelligence digest distributed to a broad cross-section of stakeholders. You will make fast, well-calibrated decisions, often with incomplete or imperfect information, and communicate those decisions clearly to the rest of the Research team and to leadership, sometimes at short notice. You will apply structured analytical judgment to response decisions, assessing exploit maturity, likely attacker interest, and blast radius with clear confidence calibration.
Analytical Products and External Content
- Beyond the daily cycle, you'll produce deeper analytical products including threat assessments with confidence ratings and documented methodology, actor and campaign profiles, and focused analytical notes on individual CVEs or exposure conditions. You'll also write blog posts for the Tenable blog, coordinate with public relations and product marketing on media responses, and contribute to webinars and conference presentations.
Standards and Tooling
- As a senior contributor, you will provide input into the classification standards, analytical methodologies, and source management practices that underpin the team's work. You will identify opportunities for improvement based on your experience executing the daily production cycle and collaborate with other team members on evolving these capabilities, including AI-assisted workflows that extend the team's analytical capacity.
Stakeholder Engagement
- Contribute to development and execution of the team's strategic priorities, respond to internal intelligence requests from product teams and leadership, collaborate with stakeholders to understand their intelligence needs, and bring those perspectives back to the team to help inform analytical priorities and areas of focus beyond daily response work.
What You'll Need:
- 7+ years in cybersecurity: with meaningful depth in at least two of: vulnerability research, threat intelligence analysis, security operations, or exposure/risk management.
- Experience producing structured analytical intelligence: Beyond just advisories or blog posts, including multi-source assessments with confidence ratings and documented methodology. Experience with intelligence community analytic standards is preferred.
- Deep familiarity with the vulnerability lifecycle: Understanding the state of a vulnerability from disclosure through exploitation. You understand CVSS scoring, exploit maturity, CISA KEV, and the difference between a vulnerability existing and an exposure being actively exploited in the wild.
- Strong understanding of threat actors and campaigns: attribution frameworks, TTP mapping (MITRE ATT&CK), and the ability to classify threats at the right level without inflation.
- Excellent analytical writing and verbal communication skills: This role produces significant written output daily including threat briefs, assessments, digests and blog posts.
- Comfort with structured, repeatable processes: This role involves executing within a structured and governed production cycle with quality gates and standards, not just ad hoc research.
- Sound judgment under uncertainty: Rapid analytical decisions with incomplete information, with honest confidence calibration. This role requires the ability to work independently and make informed decisions with accuracy and precision.
- Ability to manage multiple concurrent workstreams and shifting priorities.
And Ideally:
- Intelligence community or military intelligence background including structured analytic techniques, source reliability assessment, analytic confidence frameworks.
- Experience in vulnerability management programs at scale, and understanding of what SOC teams actually need from threat intelligence to prioritize.
- Familiarity with exposure management concepts with an understanding of the shift from vulnerability-centric to exposure-centric risk management, including cloud, identity, and OT/IoT.
- Experience with AI-assisted analytical workflows. The team uses AI extensively as an analytical co-pilot.
- Scripting and automation skills (Python, Bash, or similar) for data processing and tooling. This is not a software engineering role, but the ability to work with APIs and automate tasks is valuable.
- Demonstrated experience writing for external audiences including blog posts, conference talks, media interviews and technical papers.
- Experience with Tenable products (Tenable One, Nessus, Security Center) or competitive vulnerability management platforms.
- Familiarity with exposure surface analysis tools (Shodan, Censys, GreyNoise) and OSINT techniques.
- Experience with production tracking systems (Jira, Confluence) in a daily-cadence operational context.
- Background in non-CVE exposure domains (e.g., cloud security, identity and credential exposure, shadow AI, OT/IoT).
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We're committed to promoting Equal Employment Opportunity (EEO) at Tenable - through all equal employment opportunity laws and regulations at the international, federal, state and local levels. If you need a reasonable accommodation due to a disability during the application or recruiting process, please contact [email protected] for further assistance.
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Salary Context
This $137K-$183K range is below the median for Research Engineer roles in our dataset (median: $192K across 50 roles with salary data).
View full Research Engineer salary data →Role Details
About This Role
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
The role sits at a unique intersection. You need to understand the math well enough to implement novel architectures correctly, and you need the engineering chops to make them run efficiently on distributed systems. When a research scientist has a breakthrough idea, you're the person who turns it from a notebook prototype into a training pipeline that runs on 256 GPUs.
Across the 9,653 AI roles we're tracking, Research Engineer positions make up 2% of the market. At Tenable, this role fits into their broader AI and engineering organization.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
What the Work Looks Like
A typical week involves: implementing a new attention mechanism from a recent paper, profiling and optimizing a training pipeline that's bottlenecked on data loading, building evaluation infrastructure for a new benchmark, debugging distributed training issues across a GPU cluster, and pair-programming with a research scientist on their latest experiment. The work is deeply technical.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
Skills Required
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Compensation Benchmarks
Research Engineer roles pay a median of $252,000 based on 419 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($160K) sits 36% below the category median. Disclosed range: $137K to $183K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and AI Engineering Manager ($251,622). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Tenable AI Hiring
Tenable has 2 open AI roles right now. They're hiring across AI/ML Engineer, Research Engineer. Positions span Columbia, MD, Boston, MA, US. Compensation range: $183K - $183K.
Location Context
AI roles in Boston pay a median of $215,000 across 323 tracked positions.
Career Path
Common paths into Research Engineer roles include Software Engineer, ML Engineer, Research Intern.
From here, career progression typically leads toward Senior Research Engineer, Research Scientist, ML Architect.
This is one of the best entry points into AI research without a PhD. Build a strong engineering portfolio with ML projects, contribute to open-source ML frameworks, and demonstrate that you can implement complex ideas correctly and efficiently. The transition to Research Scientist is possible with published first-author work, which some research engineer roles support.
What to Expect in Interviews
Technical screens test both engineering skill and research understanding. Expect coding rounds with performance-critical implementations (GPU optimization, efficient data loading). Be prepared to discuss papers relevant to the team's research area and explain how you'd implement key ideas. System design questions focus on training infrastructure: distributed training, experiment tracking, and compute resource management.
When evaluating opportunities: Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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