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About This Role
### Company Background
Censys’ mission is to be the one place to understand everything on the internet. Frustrated by the lack of trustworthy Internet intelligence, we set out to create the industry’s most comprehensive, accurate, and up\-to\-date map of the Internet. Today, Censys delivers real\-time Internet intelligence and actionable threat insights to global governments, over 50% of the Fortune 500, and leading threat intelligence providers worldwide.
At Censys, we’re on a mission to provide best in class internet visibility and intelligence to the global security community. Our platform helps security teams uncover hidden threats, gain actionable insights, and build proactive defense strategies. Trusted by organizations worldwide, Censys cuts through the noise to surface the most critical risks, enabling teams to respond faster, and stay ahead of attackers. We’re constantly pushing the boundaries of what’s possible to help our customers see and secure the internet more clearly than ever before.
Location:
This position is remote within the United States or Canada.
Role Summary:
Censys is seeking a Senior Software Engineer to join our SOC/TH team focused on AI and LLMs. The SOC\-TH team builds intelligence\-driven investigation workflows on top of the Censys Platform, helping analysts, threat hunters, detection engineers and incident responders triage, identify, analyze, and monitor malicious infrastructure at internet scale. We are defining Censys as the internet intelligence layer for the modern SOC by delivering deep third\-party enrichments and real\-time adversary infrastructure visibility to analysts in a single pane.The team transforms global internet scan data, threat intelligence, into AI\-powered actionable insights. You'll work closely with product and across engineering teams dedicated to understanding customers’ business needs and to translate complex requirements into well\-architected AI enabled interactions within our platform. By fostering a culture of learning and cross collaboration, we aim to build secure infrastructures tailored to our customers' unique requirements. You’ll help shape how the world sees the Internet building AI\-powered experiences that make Censys platform smarter, sharper and more intuitive to explore every signal and threat.
What You’ll Do:
- Rapidly prototype and deploy AI\-powered features to enhance search, improve recommendations, and automate workflows across the Censys Platform.
- Apply machine learning to streamline workflows, personalize experiences, and surface critical insights
- Develop and fine\-tune LLMs and RAG pipelines to reason over Internet\-scale data, summarize findings, and deliver context\-aware insights.
- Build, scale and maintain AI\-driven analytics and automation systems helping users make faster, data\-backed decisions
- Collaborate with frontend, product, and data engineering teams to integrate AI\-native experiences that feel seamless, intuitive, and secure
- Continuously evaluate and improve model performance through RAGAS, LangSmith, and automated regression testing frameworks
What You’ll Bring:
- 5\+ years of software engineering experience, including 2\+ years building and scaling AI\-powered user\-facing features
- Strong proficiency in Python for backend and API services development
- Familiarity with RAG evaluation frameworks (e.g., RAGAS, LangSmith) and designing regression testing for LLM pipelines
- Ability to rapidly prototype, validate and refine AI features in a fast\-paced, experiment\-driven environment
- Prior knowledge in prompt engineering improving LLM accuracy, reasoning and consistency
- Understanding secure and responsible AI practices, implementing guardrails and safety checks to prevent misuse or data exposure
- Hands\-on experience with CI/CD pipelines, test automation, and deployment best practices for AI applications.
- Strong cross\-team collaboration to ensure AI capabilities elevate the overall user experience on the Platform
What Sets You Apart:
- Leveraging AI tools as a force multiplier to code smarter, iterate faster boosting productivity and improving product capabilities
- Advanced knowledge of AI/ML architectures, model evaluation, and applied deep learning
- Experience with retrieval\-augmented generation (RAG), vector search, and LLM fine\-tuning
- Understanding of embedding models and AI\-driven analytics.
- Proven ability to optimize AI models for low\-latency, real\-time interactions in browser or frontend environments
- Experience deploying secure AI systems in cybersecurity or other sensitive data domains
\#LI\-DNI
*For high cost of living areas (San Francisco Bay, New York City, and Seattle), the expected salary range for this position is* *$179,000 USD \- $201,000 USD**,* *plus bonus eligibility and equity.*
*For all other locations, the expected salary range for this position is* *$148,000 USD \- $192,000 USD**,* *plus bonus eligibility and equity.*
*In addition to our great compensation package, our benefits are effective on day one and include but are not limited to:* *401k match, health, vision, dental, and more! Please see our* *careers page* *for more details.*
*Our roots are in Ann Arbor, Michigan.*
*Our innovation is fueled by the team’s global persp**ectives.*
*For this role, we are open to remote employees across the continental US and Canada.*
California Privacy Rights Notice
Pursuant to the California Consumer Privacy Act (CCPA), we are providing you with notice that we collect personal information from job applicants for business purposes, including evaluating your candidacy for employment, conducting interviews, and, if applicable, completing the hiring process. The categories of information we may collect include identifiers (such as name and contact information), professional or employment\-related information (such as work history, education, and references), and other information you provide in your application. We do not sell or share your personal information. For more information on how we use and protect your personal information, and your rights under the CCPA, please refer to our Privacy Policy.
Salary Context
This $148K-$201K range is above the 75th percentile for LLM Engineer roles in our dataset (median: $156K across 8 roles with salary data).
View full LLM Engineer salary data →Role Details
About This Role
LLM Engineers specialize in building applications powered by large language models. They design RAG systems, fine-tune models, build agent frameworks, and optimize inference pipelines for cost and latency. This is the role that didn't exist three years ago and now has thousands of open positions.
The scope is broad. You might be building a customer support chatbot that needs to pull from a knowledge base of 50,000 documents, or designing an agent that can navigate a company's internal tools to complete multi-step tasks. The common thread is taking a foundation model and making it do something useful, reliably, at scale, without bankrupting the company on API costs.
Across the 4,317 AI roles we're tracking, LLM Engineer positions make up 0% of the market. At CENSYS, this role fits into their broader AI and engineering organization.
LLM Engineer is one of the fastest-growing AI job titles. Every company building AI-powered products needs people who understand the full stack: from embedding models to vector stores to inference optimization. The supply of experienced LLM engineers is thin because the field is so new, which keeps compensation high and demand strong.
What the Work Looks Like
A typical week includes: building and testing RAG pipelines (chunking strategies, embedding models, retrieval evaluation), debugging why the agent took a wrong action path, optimizing inference costs (caching, batching, model selection), and working with the product team on new LLM-powered features. You'll context-switch between deep technical work and cross-functional collaboration.
LLM Engineer is one of the fastest-growing AI job titles. Every company building AI-powered products needs people who understand the full stack: from embedding models to vector stores to inference optimization. The supply of experienced LLM engineers is thin because the field is so new, which keeps compensation high and demand strong.
Skills Required
RAG and vector databases are the most common requirements. Expect to work with LangChain or LlamaIndex, embedding models, and at least one vector store (Pinecone, Weaviate, Chroma). Python is non-negotiable. Understanding the cost/latency/quality tradeoffs between different model providers and architectures is what separates senior from junior engineers.
Fine-tuning experience is valuable for specific use cases but most production LLM work is RAG-based. Agent frameworks (LangGraph, CrewAI, custom orchestration) are increasingly important as companies move beyond simple chat interfaces. Evaluation and observability tools (LangSmith, Arize, custom dashboards) are essential for production deployments.
Look for roles that specify the production stack, mention specific use cases, and talk about cost optimization. Companies that understand LLM engineering will mention evaluation methodology, latency requirements, and scale targets. Vague 'build AI features' postings often mean they haven't figured out their architecture yet.
Compensation Benchmarks
LLM Engineer roles pay a median of $200,500 based on 18 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($174K) sits 13% below the category median. Disclosed range: $148K to $201K.
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.
CENSYS AI Hiring
CENSYS has 1 open AI role right now. They're hiring across LLM Engineer. Based in Remote, US. Compensation range: $201K - $201K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into LLM Engineer roles include Software Engineer, ML Engineer, Data Engineer.
From here, career progression typically leads toward AI Architect, Principal Engineer, AI Engineering Manager.
The fastest path is through software engineering. If you can build production systems and you understand LLM capabilities and limitations, you're already qualified for most roles. Build a portfolio project that demonstrates RAG implementation, evaluation, and cost optimization. Open-source contributions to LLM frameworks are strong signals to hiring managers.
What to Expect in Interviews
Technical screens cover RAG architecture design, embedding model selection, chunking strategies, and retrieval evaluation. Expect questions about cost optimization: how you'd reduce inference costs by 50% without degrading quality. System design rounds often present scenarios like 'design a customer support chatbot that can access 100K documents' and evaluate your understanding of the full stack from embedding to serving.
When evaluating opportunities: Look for roles that specify the production stack, mention specific use cases, and talk about cost optimization. Companies that understand LLM engineering will mention evaluation methodology, latency requirements, and scale targets. Vague 'build AI features' postings often mean they haven't figured out their architecture 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).
LLM Engineer is one of the fastest-growing AI job titles. Every company building AI-powered products needs people who understand the full stack: from embedding models to vector stores to inference optimization. The supply of experienced LLM engineers is thin because the field is so new, which keeps compensation high and demand strong.
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.
Frequently Asked Questions
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