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About This Role
Anrok is the leading tax automation platform enabling businesses to expand globally without compliance complexity.
As the digital economy has grown 6x over the last decade, software businesses have gone from not worrying about sales tax to needing to monitor exposure, calculate rates, and file returns across 50 US jurisdictions and 100\+ countries. This creates a critical bottleneck for companies that should be able to transact with customers everywhere.
Anrok eliminates this complexity by connecting with billing and payment systems to automate tax monitoring, calculations, and filing end\-to\-end. Our unified platform handles the ever\-changing maze of tax laws at municipal, state, and federal levels—so companies can focus on growth, not compliance.
Our customers include:
- 40% of Forbes Top 50 AI companies
- 20% of Forbes Top 100 Cloud companies
- Top companies like Notion, Anthropic, and Cursor
We're making compliant digital commerce a reality for companies big and small, backed by over $100M from leading investors including Sequoia, Spark, Index, and Khosla Ventures.
We're building the agentic infrastructure layer at Anrok—the systems that let AI agents reason about tax data, take action across jurisdictions, and operate reliably at scale. This is early and foundational work: you'll be designing and shipping the primitives that the rest of the product builds on. We're looking for an engineer who pairs a strong distributed\-systems background with real, hands\-on experience building AI agent systems—someone who understands both how to make a service fault\-tolerant and what it takes to make an LLM\-powered agent reliable in production. If you want to work on distributed systems that sit at the intersection of AI and fintech, this role is for you.
In this role, you will
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- Design and build the distributed, fault\-tolerant infrastructure that powers Anrok's AI agent capabilities—covering memory, state management, and execution for autonomous systems.
- Own technical architecture decisions for new agent infrastructure services end\-to\-end, from initial design through production deployment and iteration.
- Build the evaluation, observability, and reliability tooling that lets the rest of the team ship agents confidently on top of your infrastructure.
- Collaborate closely with product engineers and our tax data team to translate real compliance workflows into reliable, scalable agent primitives.
- Drive engineering best practices across the team: code quality, testing discipline, system design, and operational health.
- Identify and fix performance, reliability, and scalability bottlenecks as our agent systems grow.
- Help grow the team—mentoring engineers, contributing to technical culture, and raising the bar on how we build.
What excites us
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- You have a strong systems engineering background, with 3\+ years of experience building and operating distributed, production\-grade systems.
- You've designed services that are scalable, resilient, and fault\-tolerant—and you've done it more than once.
- You've built and deployed AI agents or LLM\-powered systems in production, and you understand firsthand what makes them reliable and maintainable at scale.
- You're familiar with the modern AI stack—prompt engineering, eval frameworks, RAG pipelines, tool calling, and agent orchestration—and you have a point of view on what the infrastructure beneath it should look like.
- You think carefully about architecture and tradeoffs, and you're comfortable leading technical decisions without a complete playbook.
- You have experience with the full software development lifecycle: design, code review, testing, deployment, and on\-call ownership.
- You're energized by ambiguity—you can take a fuzzy problem, break it down, and ship something real.
- You're curious and motivated to learn new tools—you've experimented with AI in your work or on your own, you're excited about what's possible, and you've built something with it (an application, a workflow, a creative solution to a real problem) that you can walk us through.
What we offer
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- The equity upside of an early\-stage startup with the product\-market fit of a later\-stage company.
- Daily lunch and snacks for those working out of our San Francisco office.
- Medical, dental, and vision insurance covered 100%.
- One Medical membership covered, flexible sick benefits and more.
- Annual learning and development stipend for books, online courses, and conferences, as well as a curious team to share your learnings with.
- Annual team offsites and in\-person opportunities around our growing Anrok hubs.
- Home office setup stipend to ensure you have the equipment you need to thrive at work.
At Anrok, we embrace a dynamic and flexible hybrid work environment based out of our growing office hubs \- San Francisco, New York City, and Salt Lake City where we collaborate in\-person 3 days per week.
For employees located outside of these hub cities, we offer a fully remote setup U.S only.
*Please be aware that Anrok does not recruit via WhatsApp or any third\-party messaging platforms. All official communication from our team comes exclusively from @**Anrok.com* *email addresses. If you receive a suspicious message claiming to be from Anrok, please do not engage. All open roles are listed on this page.*
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 3,823 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Anrok, 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 $232,000 based on 797 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
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.
Anrok AI Hiring
Anrok has 1 open AI role right now. They're hiring across AI Software Engineer. Based in San Francisco, CA, US.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,168 tracked positions. That's 26% 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 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).
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 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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