Interested in this AI Software Engineer role at Cribl?
Apply Now →Skills & Technologies
About This Role
Join the company that's building the telemetry infrastructure for the AI era. At Cribl, we partner with IT and Security teams at many of the world's biggest enterprises, including half of the Fortune 100, to bridge the gap between AI ambition and infrastructure reality. As the AI Platform for Telemetry, we give customers the choice, control, and flexibility to manage and analyze telemetry for both humans and agents, so they can build what's next.
We're one of the fastest‑growing private companies and a leading player in a massive, fast‑moving market. With a global workforce, we're remote‑first and grounded in a simple idea: software is a people business. Cribl is the place where curious, collaborative people can do their best work, grow fast, and bring their full selves to the herd.
Why You'll Love This Role
You will work closely with the founding team and a group of highly\-skilled engineers to shape the future of search and analytics of observability data. You will play a central role in bringing integrating cutting\-edge Generative AI technologies with the Cribl Product suite to help solve real customer problems. You will work closely with development partners and key stakeholders to iteratively design, develop, and deliver products and surfaces that will delight our customers. On top of it all you will have fun. Cribl strives to be a great place to work for everyone.
As An Active Member Of Our Team, You Will...
- Productionize, launch, and operate AI\-based technology integrations into Cribl's core products with the goal of solving real customer problems
- Partner with product \& design leaders to prototype and experiment with new AI features
- Stay up\-to\-date with the latest AI technologies and trends
- This position will require stand\-by, on\-call, or off\-hours duties
If You've Got It \- We Want It
- 5\+ years of professional software engineering experience building AI features end to end in production across product, backend, and model integration layers
- Proven fullstack experience, including designing and scaling React\-based UIs and backend services or APIs, with strength in backend and systems thinking
- Deep expertise in TypeScript or JavaScript plus experience with at least one backend language or runtime such as Node.js, Go, Java, or similar
- Professional experience building AI agent\-driven product experiences, including conversational interfaces, tool\-calling patterns, RAG, prompt engineering, evaluation or guardrail techniques, and agent orchestration connected to reliable backend and data systems
- Professional experience with Model Context Protocols (MCPs), agent frameworks, orchestration layers, and integrating external tools and data sources into LLM\-based systems
- Machine learning experience, ideally in applied settings where models or AI systems were shipped, integrated, evaluated, or operated in production
- Familiarity with data and infrastructure patterns for AI systems, including databases, APIs, observability, and integrating external tools, services, and data sources in production environments
- Ability to problem\-solve from first principles, make sound engineering trade\-offs aligned with product and business goals, and drive work independently through ambiguity
- Excellent communication skills, both verbal and written, with the ability to explain complex technical topics to cross\-functional stakeholders in a remote or distributed environment
\#LI\-JB1
\#LI\-Remote
Bring Your Whole Self
Diversity drives innovation, enables better decisions to support our customers, and inspires change for the better. We're building a culture where differences are valued and welcomed, and we work together to bring out the best in each other. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or any other applicable legally protected characteristics in the location in which the candidate is applying.
Interested in joining the Cribl herd? Learn more about the smartest, funniest, most passionate goats you'll ever meet at cribl.io/about\-us.
Salary Context
This $185K-$215K range is above the median for AI Software Engineer roles in our dataset (median: $185K across 231 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 4,317 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Cribl, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $218,500 based on 729 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($200K) sits 8% below the category median. Disclosed range: $185K to $215K.
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.
Cribl AI Hiring
Cribl has 1 open AI role right now. They're hiring across AI Software Engineer. Based in San Francisco, CA, US. Compensation range: $215K - $215K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% above the national median.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
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).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.