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About This Role
Set the standard for how *arrivia* builds with AI agents, and stay hands\-on enough to build it yourself.
About the Role
As a Principal Agentic Software Engineer, you are a hands\-on, product\-minded, customer\-focused full\-stack engineer who sets technical direction across multiple products and platforms and personally builds the primitives other teams depend on. You still open the editor yourself, and you are a driving force behind how agentic development works at *arrivia*.
You do not advise from the sidelines. You direct AI coding agents through real, multi\-step work, decide what becomes shared infrastructure, and make the build\-vs\-buy calls that shape our roadmap. Operating with broad autonomy and cross\-organizational accountability, you own multiple domains or a critical platform.
Your work reaches millions of people. We build the travel and rewards platforms behind major banks, membership organizations, and travel brands, where members book real trips with real money and points, so trust is the product. The operating rhythms, launch gates, and quality standards you set let dependent teams move faster while protecting that trust.
You will balance short\-term partner requests against long\-term member trust, lead the one\-way\-door decisions in your areas, and define how AI coding agents reshape the way we build and operate at scale. You work across many technology domains, business teams, and offices in a fast\-paced, highly collaborative environment.
What You'll Own
- Technical direction: Set direction across multiple products and platforms, including AI infrastructure, data platforms, and shared primitives, and personally build the components that increase leverage for teams beyond your own.
- Agentic engineering practice: Co\-define our prompt libraries, MCP server patterns, quality gates for AI\-generated code, and human\-agent collaboration patterns, then get them adopted.
- MCP integrations: Architect Model Context Protocol integrations that give AI agents secure, governed access to internal APIs, data sources, and platform services across teams.
- Standards and simplification: Drive build\-vs\-buy decisions and convergence on strategic standards that reduce complexity rather than add to it.
- Operating rhythms: Institutionalize prioritization mechanisms, AI launch checklists, and SLOs that raise quality and speed for dependent teams.
- High\-stakes decisions: Lead one\-way\-door decisions in your areas, minimize the chance of failure, and hold contingency plans for failure scenarios.
- ROI and focus: Guide teams on what is worth pursuing and what to kill, and define what success looks like for your domains.
- Data and evaluation culture: Standardize canonical metrics, AI eval frameworks, minimum sample sizes, and guardrail dashboards, and hold teams to them before anything scales.
- Responsible AI: Co\-define responsible\-AI guardrails and review processes so high\-risk use cases get scrutiny and post\-incident learnings feed back into design standards.
- Modernization: Turn legacy systems into scalable, cloud\-native, agent\-ready services through clear migration paths and architectural patterns.
- Trust and safety: Safeguard members' sensitive data by defining security patterns and partnering with our Risk, Security, and Compliance teams.
- People and norms: Mentor staff\-level engineers and shape technical\-leadership norms across individual contributors, so strong solutions emerge from teams and not from you alone.
What You'll Bring
You do not need every item below. We are excited by candidates with real strength across several of these areas.
- A recent, hands\-on track record. You have personally built and shipped working software in the last year or two, not only led teams who did.
- 8 to 12 years of full\-stack development and architecture experience building web applications and services, including technical leadership beyond a single team.
- A bachelor's degree in Computer Science, Computer Engineering, or equivalent experience.
- Advanced, hands\-on use of AI coding agents such as Claude Code, Cursor, or GitHub Copilot, including directing them through larger multi\-step work, with a track record of building practices that others adopt.
- Strong proficiency in two or more of TypeScript/JavaScript, Python, and C\#/.NET, plus modern frontend frameworks such as React, Next.js, Vue, or Angular.
- Deep experience with RESTful API design, distributed systems, and microservices at scale, and cloud\-native delivery on AWS, Azure, or GCP including multi\-region or hybrid deployments.
- Hands\-on depth with the Model Context Protocol, prompt engineering for code, and AI building blocks such as Azure AI Foundry, RAG, LangChain, or graph\-based data technologies like GraphRAG and GraphDB.
- Production experience with real\-time streaming and distributed messaging such as Redis, Kafka, or RabbitMQ, plus containerization and orchestration with Docker and Kubernetes, advanced CI/CD, infrastructure\-as\-code, and modern DevOps or SRE practices.
- A record of mentoring senior engineers and raising the bar through frameworks and standards, and of explaining complex AI and architectural ideas simply to executives and non\-technical partners.
Benefits \& Perks
- Unlimited PTO
- Exclusive employee travel rates
- Travel discounts through *arrivia* programs
- Medical, dental, and vision insurance
- 401(k) with company participation
Who We Are
Why *arrivia*
At *arrivia*, we power travel loyalty and rewards programs for some of the world's leading brands. Our teams help millions of travelers book memorable experiences while delivering innovative technology and travel solutions to our partners. With a global workforce and a culture built on curiosity, ownership, authenticity, and collaboration, we're creating the future of travel.
Equal Opportunity Employer
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At arrivia, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 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.
arrivia AI Hiring
arrivia has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Scottsdale, AZ, 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/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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.
Frequently Asked Questions
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