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About This Role
The exciting world of scientific research is fueled by people with a passion for solving complex problems. At Cayuse, we are committed to our customers' success by empowering organizations to conduct globally connected research that advances their impact on science, discovery and society. We build on that commitment with proven, integrated and easy-to-use technology that delivers exceptional value, and world class service and support that accelerates outcomes.
But we are more than just an empowering platform powered by advanced technologies. We are a collaboration of exceptional, highly skilled people with multi-disciplinary expertise, and are building our team to support our ambitious growth plans. Cayuse's foundational strength comes from our customer and employee focused values and commitment to industry-leading solutions. It's an exciting time to become a key member of our growing team.
Join our remote first culture as an Associate Engineer, an entry-level, hands-on engineering role focused on building AI capabilities directly into Cayuse products. You will work alongside senior engineers to design, build, test, and ship AI agents, Model Context Protocol (MCP) servers, and LLM-powered features that help research institutions and their administrators work faster and with less manual effort. This is not a data science or ML research role. It is a software engineering role for someone early in their career who writes solid code, learns quickly, and wants to grow into a strong AI engineer by shipping real features to real customers.
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Responsibilities
As an Associate Engineer, you will contribute across the full development cycle of AI-powered product features, with mentorship and code review from senior engineers. Success in this role means delivering well-tested, maintainable code, understanding the problems you're solving, applying feedback, and progressively taking on more complex engineering responsibilities.
- AI Agent Development: Build and maintain AI agents that use large language models (such as Claude) to complete multi-step tasks within Cayuse products — calling tools, retrieving data, and taking actions on behalf of users. Implement prompts, tool definitions, and orchestration logic under the guidance of senior engineers.
- MCP Server Development: Help build and extend Model Context Protocol (MCP) servers that allow AI assistants to securely interact with Cayuse products. Work with senior engineers to implement tools, APIs, authentication, and permission checks using established engineering standards. Write clear documentation and tests to ensure these integrations are reliable and reusable.
- Hybrid AI Validation: Help turn AI-extracted rules, such as requirements parsed from funding opportunity instructions, into deterministic, auditable validations that run alongside existing form rules and are reviewed by people before use.
- Product Integration: Integrate AI features into existing Cayuse applications through platform APIs. Build on the shared capabilities and patterns set by the architecture team instead of one-off implementations, and partner with product teams so AI fits naturally into customer workflows.
- Testing & Evaluation: Write unit and integration tests for AI features, and help build evaluation sets that measure the accuracy, reliability, latency, and cost of LLM outputs. Contribute to time-boxed AI pilots and help document whether each should go, pivot, or stop. Investigate failures, identify root causes, and propose fixes.
- Deployment & Operations: Ship code through CI/CD pipelines to cloud environments (AWS, including Amazon Bedrock). Add logging and monitoring to AI services and help troubleshoot production issues.
- Learning & Collaboration: Participate in code reviews, design discussions, and sprint ceremonies. Keep current with the fast-moving AI tooling ecosystem and share what you learn with the team.
- Governed AI Platform: Develop AI features using Cayuse's shared AI platform and established engineering standards. Learn how our platform handles model access, security, tenant isolation, usage tracking, and auditing. Work with senior engineers to ensure new features follow these patterns rather than introducing one-off solutions.
- Responsible AI & Security: Follow established security and privacy standards when developing AI features. Learn to recognize and test for risks such as prompt injection, unauthorized data access, and inaccurate AI outputs. Work with senior engineers to ensure AI features respect user permissions, protect customer data, and maintain proper tenant isolation. Raise potential security or reliability concerns early.
Qualifications
Core Experience
- 0–2 years of professional software development experience, including internships, co-ops, or apprenticeships.
- Demonstrated ability to build working software through coursework, internships, personal projects, open-source contributions, or hackathons. A portfolio or GitHub profile is strongly encouraged.
- Exposure to team-based development using version control (Git), code review, and agile practices.
- BS in Computer Science, Software Engineering, or a related field, or equivalent practical experience.
Technical Skills
- Software Development: Proficient in at least one general-purpose language (Python or TypeScript/JavaScript preferred; Java with Spring Boot also valued). Solid grasp of data structures, APIs, error handling, and writing tests.
- AI & LLM Fundamentals: Demonstrated interest in AI technologies and how they can be applied to software development. Experience experimenting with LLM APIs, prompt engineering, tool calling, or retrieval-augmented generation (RAG) through coursework or personal projects is a plus. A willingness to learn and understand how these technologies work is more important than prior experience with any specific AI framework.
- Agents & MCP: Familiarity with the Model Context Protocol, agent frameworks, or AI coding assistants (such as Claude Code or GitHub Copilot).
- APIs & Integration: Working understanding of REST APIs, JSON, and authentication basics (API keys, OAuth).
- Cloud & Infrastructure: Exposure to a cloud platform (AWS preferred; Amazon Bedrock a plus), containers (Docker), or CI/CD pipelines is a plus.
- Data Fluency: Comfortable writing basic SQL queries and working with structured data. Exposure to Snowflake or another cloud data warehouse is a plus.
Collaboration & Communication
- Clear communicator — writes readable code, documentation, and pull request descriptions, and raises questions early when blocked.
- Strong problem-solving instincts: you break ambiguous tasks into smaller steps and work through them methodically.
- Coachable and growth-oriented — you seek out feedback and apply it.
- Collaborative and adaptable — effective in a remote-first environment and when coordinating across offshore, nearshore, and onshore teams.
- Genuine curiosity about how AI is changing software and knowledge work, paired with practical judgment about where it adds real value.
Benefits
- Competitive Medical Benefits (PPO + HSA available)
- Vision, Dental, Short-Term Disability fully covered by Cayuse
- Unlimited PTO + Holidays + Flexible Work Schedule
- Remote Work Stipend
- Equal Paid Parental Leave
- 401k with Employer Matching
- Quarterly Wellness Reimbursement
- Remote Work Environment, supporting the Ultimate Employee Experience
Cayuse does not accept agency resumes. Please do not forward resumes to our jobs alias or any Cayuse employees. Cayuse is not responsible for any fees related to unsolicited resumes.
Our culture is one of inclusion and belonging where everyone feels respected, treated justly, supported and nourished. We all share responsibility for creating and sustaining a work environment where differences are celebrated and we are empowered to strive for excellence. We're proud to be an equal opportunity employer and actively seek to recruit, develop, and retain a diverse and talented workforce.
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 9,079 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At Cayuse, 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 $220,000 based on 1,601 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $111,000.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Cayuse AI Hiring
Cayuse has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Atlanta, GA, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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