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About This Role
About Us:
At Reevo, we're reimagining the entire revenue stack from the ground up, and we're doing it with speed. We're building software that orchestrates every go\-to\-market motion, enabling B2B teams to operate faster, smarter, and more collaboratively. By combining automation, intelligence, and a radically intuitive interface, we're helping companies unlock new levels of productivity and growth across marketing, sales, ops, and customer success teams.
If you're excited about working on a product that reshapes how revenue teams work and being surrounded by curious, driven teammates, you'll feel right at home here. From day one, you'll get real ownership, real mentorship, and real impact. Our team of 50\+ builders has 30 exits under their belt, so you'll be in good company, and working alongside the best!
About the Role:
Our team is looking for experienced AI\-focused Software Engineers who are passionate about building intelligent systems that solve real user problems. The ideal candidate brings deep technical expertise in AI/LLM systems and strong software engineering fundamentals, with experience developing production\-ready AI applications from experimentation to deployment. In this role, you’ll collaborate with engineering, product, and data teams to design and implement AI\-powered features and systems. You’ll apply your AI expertise and practical engineering skills to build reliable applications, improve system performance, and deliver intuitive user experiences that directly impact our users.
What You’ll Do:
- Design and implement retrieval augmented generation (RAG) systems for contextual AI responses
- Build evaluation platforms and frameworks to measure AI system performance and quality
- Develop novel interaction patterns and user experiences for large language model integrations
- Build systems for data collection, labeling, and continuous improvement of AI models
- Collaborate closely across teams to identify opportunities where AI can create meaningful user value
- Build reusable AI infrastructure and platforms that enable rapid AI feature development
- Implement AI safety measures, monitoring, and quality assurance systems
- Present AI capabilities and demos to internal teams and gather feedback
- Stay current with latest AI/ML research and incorporate relevant advances into products
What You Bring:
- 5\+ years of software engineering experience, with significant focus on building AI/ML systems
- Strong expertise in large language models, RAG systems, and modern AI frameworks
- Experience with ML operations, model deployment, and AI system monitoring
- Proven experience building production AI applications, from experimentation to deployment
- Deep understanding of prompt engineering, fine\-tuning, and AI evaluation methodologies
- Proficiency in analyzing AI system performance and implementing improvements
- Experience with vector databases, embedding systems, and semantic search
- Knowledge of AI safety practices and responsible AI development
- Strong communication skills, with the ability to explain AI concepts to technical and non\-technical stakeholders
What we offer:
- Compensation: A highly competitive base salary and bonus structure, and early\-stage equity that aligns your success directly with the company's growth.
- Comprehensive benefits: health, dental, and vision coverage, generous paid time off (PTO), plus additional benefits that support how you work.
- Growth and development: Career advancement paths, dedicated mentorship opportunities, and a strong commitment to investing in your continuous professional development and skill enhancement.
- Dynamic culture: Join a collaborative, innovative, and fast\-paced work environment where your direct contributions have a tangible and immediate impact on the product, the sales strategy, and the overall company trajectory.
Here at Reevo, we know the best ideas come from people with different experiences and perspectives. We welcome candidates from all backgrounds and are proud to be an equal opportunity employer. We do not discriminate based on any protected characteristic, and we’re happy to provide accommodations throughout the application process.
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 Reevo, 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.
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.
Reevo AI Hiring
Reevo has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Santa Clara, CA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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.
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