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About This Role
Hyperproof is reinventing how engineering teams build software in the age of AI. We're looking for a Software Engineer Intern to join our Engineering team for Summer 2026 and help us push the frontier of AI\-assisted development : from agentic coding workflows and automated code review to AI\-powered observability and developer experience metrics. You'll work alongside experienced engineers shipping real tooling that other Hyperproof engineers depend on every day, while still getting plenty of hands\-on, small\-scale software engineering work because that's how you actually learn how things fit together.
Hyperproof runs a multi\-tiered architecture on Azure Kubernetes Service, with a tech stack including TypeScript, Node.js, React, Java, C\#, and PostgreSQL ; so there's no shortage of surface area to learn and contribute across.
KPIs FOR THIS ROLE
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- Ship measurable improvements to developer productivity (e.g., PR cycle time, CI duration, AI\-assisted PR throughput)
- Write clean, well\-tested code with thoughtful unit and integration coverage
- Contribute to Hyperproof's internal developer documentation, CLAUDE.md skill files, and MCP server configurations
WHO YOU ARE
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You are currently pursuing a degree in Computer Science, Computer Engineering, or a related applied sciences discipline. You're genuinely curious about how AI is changing software engineering and want to be on the team building the tools, not just using them.
As a Software Engineer Intern on the AI Developer Productivity team, you'll build internal tooling and automation that leverages LLMs, agentic workflows, and modern developer platform tech — and you'll do small, well\-scoped feature work in our products to ground your understanding in how real systems behave.
This is a fully remote internship with a fully remote interview and onboarding process.
WHAT WE'RE LOOKING FOR YOU TO DO AND LEARN
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- AI\-Assisted Code Generation \& Review : Work with Claude Code and GitHub Copilot day\-to\-day, and help evaluate tools like CodeRabbit for automated code review and PR summarization
- AI Agent Development : Help build automated agents for customer bug triage, PR regression classification, and error\-to\-fix workflows that chain multiple AI agents together
- Developer Context \& Tooling : Author and maintain CLAUDE.md skill files, MCP server configurations, and prompt engineering improvements that raise the quality of AI output across the team
- CI/CD Optimization : Contribute to test impact analysis, flake quarantine, pipeline parallelization, and build acceleration to support higher AI\-driven PR throughput
- Engineering Platform for AI : Help provision multi\-environment dev setups, authenticated MCP servers, and connected documentation that enable agentic coding workflows
- Metrics \& Measurement : Build dashboards and instrumentation tracking PR throughput per developer, Claude usage rates, code coverage, and developer experience sentiment
- AI\-Powered Observability : Explore using AI to automatically diagnose production errors from telemetry and propose or generate fixes
- Foundational engineering work : Alongside the AI\-focused projects, you'll get scoped feature and bug\-fix work in our TypeScript/Node.js/React frontend and Java/C\# backend services — because the best way to understand a developer productivity problem is to be a developer working in the system
- Work directly with a mentor on real problems, write automated tests, and document what you build
WHAT YOU'LL BRING
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- Hands\-on familiarity with at least one AI coding assistant (Claude Code, Copilot, Cursor, or similar) — and opinions about where they shine and where they fall over
- Solid fundamentals in at least one modern language (TypeScript, Python, Java, C\#, or Go) and comfort with Git and the command line
- Curiosity about prompt engineering, agentic systems, MCP, and the broader question of how to make engineers more effective
- Familiarity with REST APIs and a willingness to learn new technologies quickly
- Strong written communication — a lot of AI productivity work is fundamentally about clearly conveying context, both to humans and to models
- Bonus: any experience with CI/CD systems, observability tooling, or building developer tools
LOCATION
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- We are a fully remote company. Rather than restrict ourselves to talent in one city, we'd rather find the best people regardless of where they live.
CANDIDATE EXPERIENCE
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- We value your time and want you to know what to expect from us.
- For this internship, expect a 45\-minute initial chat with our Senior Technical Talent Partner, a take\-home technical assessment, and three 45\-minute 1:1 conversations with members of our engineering team.
- You'll have space to ask the questions that matter to you, and we'll get to know you and think carefully about the right fit on our team.
WHERE YOU'LL GO
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- Hyperproof loves internal transfers. If a linear career path isn't what you're looking for, you can work with your manager and our people team to explore lateral moves across the organization as you grow with us.
WHY YOU'LL LOVE HYPERPROOF
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- It's an exciting time to be at Hyperproof. We're investing heavily in AI\-augmented engineering, and interns here get to work on problems that genuinely move the needle for the team — not just busywork.
- Hyperproof is a radical new approach to helping organizations discover, track, and meet the web of compliance, business, and government requirements they face. We're disrupting the governance, risk, and compliance software space with products built by a team that cares about cloud software, great user experience, simplicity, and positive social impact.
WHERE YOU'LL GO
- Hyperproof also loves to see an internal transfer. If a linear career path is not what you're looking for, you can work with your manager and our people team to explore lateral moves to other parts of the organization as you continue to grow with us.
WHAT WE OFFER TO OUR EMPLOYEES
Please note: Benefits listed below are for employees in the United States; contractor roles or international positions may differ
- Annual compensation reviews \+ equity
- Unlimited PTO: strongly encouraged to unplug and recharge
- Health: coverage for medical, dental, and vision \- employee and dependents
- 401K, which vests immediately, complete with a 4% company match
- 12 weeks of Parental leave and 1 year free diapers and wipes with Honest
- Annual company in\-person events and quarterly in\-person connects
- $500 home office stipend \- at the time of hire. Any additional home office needs are requested as needed.
- $100 quarterly paid wellness stipend
- Pet insurance discount
- Slack channel notifications turn off after 5 pm based on your time zone
- Two Hypercharge weeks of rest where we close company\-wide (July \& Dec)
It's an exciting time to be at Hyperproof — we recently raised $40 million in our Series B financing, further cementing Hyperproof as the emerging leader in the risk and compliance management space.
At Hyperproof's core are our passionate team members who focus on user experience, beautiful design, and evangelize a positive social impact of our cloud based platform. We help organizations streamline their risk and compliance workflows so our customers can spend more time strategically managing programs and less time wrangling spreadsheets.
We are disrupting the governance, risk, and compliance software space with our innovative platform by helping traditionally unsung heroes (compliance professionals) do the right things so the wrong things don't happen.
Learn more about the @hyperproof culture and how it all started.
A NOTE ABOUT OUR INTERVIEW PROCESS
We're committed to creating a fair, respectful, and secure hiring experience for everyone. As part of that commitment, we use standard verification steps throughout our interview process.
Here's what that means for you:
- We may conduct routine verification checks during the hiring process.
- You might be asked additional questions to better understand your experience and background.
- For video interviews, we ask that candidates be on camera without filters or visual modifications.
These steps are applied consistently for all candidates and are designed to ensure an equitable experience for everyone.
EQUAL OPPORTUNITY EMPLOYER
Hyperproof is committed to a diverse and inclusive workplace — it's one of our core values! Hyperproof is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
Our company is dedicated to building a diverse, inclusive, and authentic workplace. If you're excited about this role, but your experience doesn't perfectly fit every qualification, we encourage you to apply anyway. You may be just the right person for this role or others.
To ensure a smooth interview process, all candidates will be required to provide a valid phone number that is *not* a VOIP (Voice Over Internet Protocol) number. This helps us maintain clear and reliable communication throughout your interview experience.
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 3,824 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At HYPERPROOF, 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 $234,620 based on 682 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $97,380.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
HYPERPROOF AI Hiring
HYPERPROOF has 2 open AI roles right now. They're hiring across AI Software Engineer. Positions span Remote, US, Seattle, WA, US.
Location Context
AI roles in Seattle pay a median of $228,000 across 1,009 tracked positions. That's 14% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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