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About This Role
JOB DESCRIPTION
At eBay, we're more than a global ecommerce leader — we’re changing the way the world shops and sells. Our platform empowers millions of buyers and sellers in more than 190 markets around the world. We’re committed to pushing boundaries and leaving our mark as we reinvent the future of ecommerce for enthusiasts.
Our customers are our compass, authenticity thrives, bold ideas are welcome, and everyone can bring their unique selves to work — every day. We're in this together, sustaining the future of our customers, our company, and our planet.
Join a team of passionate thinkers, innovators, and dreamers — and help us connect people and build communities to create economic opportunity for all.
AI Native MTS1 Software Engineer, Risk Engineering
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### About the Team
Global Payments and Risk builds products and platforms that enable safer, more trusted commerce experiences. We use data, modern software architectures, and AI\-native engineering practices to solve risk challenges at scale across detection, decisioning, automation, and operational workflows. This team operates highly scalable systems with meaningful customer and business impact.
### Job Summary
We are seeking a talented Software Engineer to build and operate the platforms and services that enable smarter risk decisions, seamless automation, and resilient workflows at scale. In this hands\-on role, you will deliver scalable, reliable, and secure software systems that drive meaningful business impact across Risk Engineering. Engineers in this organization are expected to use modern AI\-enabled practices effectively. In this role, that includes applying AI where it can improve product quality, operational efficiency, and engineering productivity. This is a hands\-on, applied software engineering role with end\-to\-end ownership for builders who want to design, ship, and operate real systems in production.
### What You Will Do
- Design, build, test, deploy, and support production\-ready AI\-enabled systems that solve real customer and business problems in Risk Engineering.
- Build end\-to\-end services and workflows that combine LLMs, retrieval, tool use, structured outputs, deterministic logic, and human review where appropriate.
- Develop scalable backend services, APIs, and integrations that support risk decisioning, investigation, and automation use cases.
- Translate business and operational needs into clear technical designs, delivery plans, and measurable outcomes.
- Own a functional area end to end, including implementation, launch readiness, observability, monitoring, and continuous improvement.
- Define and maintain quality bars for AI behavior through task\-level evaluations, offline and online metrics, regression detection, and release criteria.
- Improve reliability, scalability, latency, security, and cost efficiency of AI\-enabled systems in production.
- Drive operational readiness through fallback strategies, rollback paths, incident handling, and runbooks.
- Partner with engineering, product, data science, analytics, and operations teams to deliver robust, well\-governed solutions.
- Contribute high\-quality code, design documents, and test plans, and raise engineering standards through thoughtful reviews and documentation.
### Minimum Qualifications
- Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
- 5\+ years of software engineering experience building and operating production systems.
- Strong proficiency in at least one backend language such as Python, Java, TypeScript.
- Experience building and shipping backend services or product features, including design, implementation, testing, launch, and production support.
- Experience building with modern AI capabilities in production or at meaningful scale, such as LLM\-based workflows, tool calling, retrieval, structured outputs, workflow automation, or knowledge\-grounded product experiences.
- Experience designing reliable AI\-powered workflows, including prompt or context design, output validation, fallback behavior, and iterative quality improvement.
- Strong understanding of APIs, service integrations, data flows, logging, monitoring, and production debugging; familiarity with distributed systems concepts.
- Demonstrated ability to make sound engineering trade\-offs across reliability, latency, scalability, cost, and developer velocity.
- Strong written and verbal communication skills, with the ability to work effectively across engineering, product, design, and other cross\-functional partners.
### Preferred Qualifications
- Experience building or contributing to agentic or tool\-using workflows with clear guardrails and bounded autonomy.
- Experience working with evaluation workflows for AI outputs, including automated graders, benchmark tasks, error analysis, or human\-in\-the\-loop review.
- Familiarity with search, retrieval, ranking, embeddings, or other knowledge\-grounded AI patterns.
- Familiarity with secure execution practices, scoped credentials, auditability, and safe handling of AI\-driven actions.
- Experience using AI to improve engineering workflows, including development, testing, debugging, or operational tooling.
- Experience instrumenting and improving systems through monitoring, experimentation, and iterative analysis of failures or regressions.
- Experience in fraud, trust, payments, risk, or other decisioning\-heavy domains is a plus.
- Demonstrated ability to collaborate effectively on small technical initiatives; mentoring experience is a plus.
### Why Join Us
You will work on high\-impact systems that help make commerce safer at scale, while building practical AI\-enabled capabilities that improve customer experience, operational efficiency, and business performance. This role is ideal for an engineer who wants to apply GenAI in production with strong engineering rigor, clear quality bars, and real\-world impact.
Additional Details
The base pay range for this position is expected in the range below:
$172,000 \- $229,600
Base pay offered may vary depending on multiple individualized factors, including location, skills, and experience. The total compensation package for this position may also include other elements, including a target bonus and restricted stock units (as applicable) in addition to a full range of medical, financial, and/or other benefits (including 401(k) eligibility and various paid time off benefits, such as PTO and parental leave). Details of participation in these benefit plans will be provided if an employee receives an offer of employment.
If hired, employees will be in an “at\-will position” and the Company reserves the right to modify base salary (as well as any other discretionary payment or compensation program) at any time, including for reasons related to individual performance, Company or individual department/team performance, and market factors.
Remote roles are not eligible for U.S. visa sponsorship.
eBay is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, sexual orientation, gender identity, veteran status, and disability, or other legally protected status. If you have a need that requires accommodation, please contact us at [email protected]. We will make every effort to respond to your request for accommodation as soon as possible. View our accessibility statement to learn more about eBay's commitment to ensuring digital accessibility for people with disabilities. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
We use cookies to enhance your experience and may use AI tools for administrative tasks in the hiring process. To learn how we handle your personal data and use AI responsibly, please visit our Talent Privacy Notice, Privacy Center and AI Hiring Guideline.
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Salary Context
This $172K-$229K range is above the median for AI Software Engineer roles in our dataset (median: $183K across 194 roles with salary data).
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 3,708 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At eBay, 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 $219,250 based on 424 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($200K) sits 8% below the category median. Disclosed range: $172K to $229K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
eBay AI Hiring
eBay has 2 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Based in San Jose, CA, US. Compensation range: $229K - $262K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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