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About This Role
Palona is building a category\-defining AI platform for restaurants. This role builds the technical growth system that helps the right restaurant owners, operators, franchise leaders, and technology buyers discover Palona, understand its value, experience the product, and become qualified opportunities.
We are looking for an AI Full\-Stack Engineer who treats growth as an engineering and product discipline. You will own web experiences, experimentation, SEO and generative\-engine optimization, paid acquisition infrastructure, attribution, analytics, and AI\-enabled growth workflows. You will work across the public website, landing pages, demos, calculators, content systems, marketing and sales integrations, and the data pipelines that connect acquisition to revenue.
The objective is not broad traffic or dashboard activity. It is a measurable, compounding system for qualified demand and pipeline. You will partner closely with Growth, Marketing, Sales, Product, and Design while remaining a production engineer with a high bar for code quality, performance, measurement, and user experience.
What you’ll own* Build fast, compelling, conversion\-focused web experiences for high\-intent restaurant buyers.
- Create and iterate on landing pages, interactive demos, ROI tools, comparison experiences, and other acquisition products.
- Establish reliable end\-to\-end attribution from source and campaign through demo booking, qualification, opportunity, and closed revenue.
- Instrument product and marketing events, persist campaign context across domains and tools, and improve data quality across the funnel.
- Design an experimentation system for messaging, offers, page structure, onboarding, calls to action, and acquisition channels.
- Improve technical SEO and GEO foundations, including performance, crawlability, metadata, structured data, internal linking, and content architecture.
- Integrate and automate workflows across analytics, CRM, scheduling, advertising, content, and sales systems using APIs and server\-side events.
- Apply AI to accelerate research, content operations, personalization, campaign analysis, and experiment generation while building appropriate review and quality controls.
- Analyze funnel behavior and unit economics, identify the highest\-leverage bottlenecks, and ship improvements rather than stopping at recommendations.
- Partner with Growth and Sales to define qualified\-conversion metrics and ensure optimization targets reflect pipeline quality, not vanity volume.
- Build reusable systems so the company can launch new product pages, vertical campaigns, and experiments quickly and safely.
Requirements
- 3\+ years of industrial experience in relevant technical domain.
- Strong full\-stack web engineering experience, ideally with TypeScript, React, Next.js, APIs, and modern deployment platforms.
- Experience building measurable user funnels, experimentation systems, analytics instrumentation, or growth products in production.
- Working knowledge of SEO, web performance, conversion optimization, paid acquisition mechanics, and attribution; deep expertise in every channel is not required.
- Ability to work with data using SQL or analytics tools and translate findings into prioritized product and engineering work.
- Experience integrating third\-party APIs and managing the reliability, consent, identity, and data\-quality challenges they create.
- Strong product taste and the ability to write or collaborate on clear, outcome\-oriented customer experiences.
- A rigorous approach to experimental design and causality; you know the difference between correlation, directional evidence, and a trustworthy test.
- AI\-native working habits and a practical view of where automation needs human judgment.
- Comfort owning business outcomes and collaborating closely with non\-engineering partners.
Benefits
- Competitive Salary and Stock Option Plan.
- Medical, dental, vision, retirement, leave, and disability benefits as applicable.
- Family Leave
- Short Term \& Long Term Disability
- Paid time off and company holidays.
- Learning and development support.
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 Palona AI, 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. Mid-level AI roles across all categories have a median of $194,400.
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.
Palona AI AI Hiring
Palona AI has 4 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Based in Los Altos, 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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