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About This Role
Location
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San Francisco HQ
Address
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1098 Harrison Street, San Francisco, California, 94103
Employment Type
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Full time
Location Type
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Hybrid
Department
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All DepartmentsEngineering
Compensation
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- Zone 1Base Salary $228,360 – $337,260 • Offers Equity
The target base salary will vary based on the job's location.
Our geographic zones are as follows:
Zone 1 \- San Francisco / New York City / Seattle
Zone 2 \- Los Angeles / Washington DC / Austin / Boston / Sacramento / San Diego
Zone 3 \- Atlanta / Portland / Chicago / Philadelphia / Denver / Miami / Dallas / Raleigh
Zone 4 \- All other US cities
The base salary range listed for this full\-time position excludes commission (if applicable), equity and benefits. The pay range shown on each job posting is the minimum and maximum target for new\-hire salaries. Actual pay may be higher or lower depending on factors like skills, experience, and relevant education or training.
We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam.
AI and intelligent systems are driving the fifth paradigm shift, following previous technological revolutions like mainframes, personal computers, the internet, and mobile devices. We believe, in the foreseeable future, AI will revolutionize the FinTech industry \- from how consumers understand and manage their finances, to how developers build applications and how all companies operate. The fintech industry landscape will undergo a fundamental reshape.
Plaid in the FinTech AI Ecosystem
Plaid is uniquely positioned to become the financial data and insights backbone for AI applications and platforms in this evolving ecosystem.
We believe consumers should be able to understand and manage their financial life through conversational AI interfaces using natural language. We believe consumers should have peace of mind with a trustworthy consent and authorization manager when agents shop for them. We believe identity verification and financial fraud prevention in AI\-powered products should feel seamless and embedded for the end users. The list goes on.
The most important AI companies, major fintechs, and customer agent platforms are actively trying to integrate Plaid into AI\-powered products and solutions they build.
Plaid’s AI\-powered Experiences for Customers
The explosive growth in model intelligence and increasing relevance for enterprise tasks offer a unique opportunity for Plaid to rethink from the ground up how we operate and interact with our customers for the most optimal experience.
Our early experiments in building AI agents for customer experience are paying off with huge business value. For example, our homegrown AI\-powered customer support agents resolve a significant proportion of customer questions and issues within seconds, which would otherwise have required humans hours to work on.
This is just the beginning and there is so much more we could do.
The AI Applications Team
You will have the opportunity to join as one of the founding members of this newly formed team that is dedicated to consolidating and rapidly scaling our successful bets so far, and grow with the team in our quest to accelerate Plaid’s transformation into an AI\-first company.
In this role you will lead projects that enable and scale our business with our largest AI customers and partners, starting with personal finance use cases and expanding into many others; examples include:
- Develop and evolve the preferred integration pattern for Plaid with AI providers \- from API adaptations to building the official Plaid MCP Servers, and beyond
- Redefine how Plaid’s consumer link experience embed into conversational interfaces in the most seamless way
- Architect the trust layer for the future of agentic commerce that will become the industry standard
Additionally you will be expected to scale and extend our existing successful bets on AI\-powered customer experience; examples include:
- Make the next step\-function improvement in our homegrown customer support agent \- land our multi\-turn and multi\-agent system that powers a truly delightful experience for our customers; define how to scalably run offline evaluation for complex multi\-turn open\-ended tasks; research and prototype how Human\-In\-The\-Loop \- Reinforcement Learning (RLHF) can power an insights flywheel; pioneer the architecture for customer\-specific long\-term memory, etc.
- Extend our agentic system to support other critical parts of the customer journey, starting with areas with the highest ROI \- top\-of\-funnel product recommendation, customer onboarding and risk diligence, customer activation and assistance for faster productionization, as well as upselling and cross\-selling of Plaid products
You will have a front row seat to all the latest industry developments. Over time, with the skills and experience you develop and hone on this team, you can become an influential voice in defining where AI \<\> Fintech will be heading longer term.
Responsibilities
- Design, develop, and maintain scalable backend services and APIs, as well as intuitive, high\-quality frontend applications that bring those systems to life.
- Work with other AI engineers, software engineers and machine learning engineers to architect, design and implement GenAI\-powered products and features
- Collaborate across functions to understand user needs, propose and implement AI\-powered solutions where they’re expected to have the highest impact
- Design and execute rapid experiments to push the boundaries on potential business impact from emerging AI capabilities, with a focus on minimal viable testing approaches
- Balance creative exploration of possibilities with rigorous evaluation of technical feasibility, product potential and business impact
Requirements
- Experience building backend services and working with microservices or service\-oriented architectures
- Hands\-on experience working with LLMs to build products and shipping them to product with iterating with real user feedback \- including but not limited to:
+ Prompt engineering
+ Fine\-tuning
+ Retrieval augmented generation (RAG)
+ Semantic search \- vector database and embedding models
+ Agent orchestration framework
+ Evaluation and monitoring framework of open\-ended tasks
+ Streaming and SSE
+ Common UX and design patterns for GenAI\-powered products
- Strong debugging and monitoring experience for production systems
- Ability to deeply understand customer and user needs through user research and rapid experimentation \- be your own technical PM
- Ability to balance divergent thinking (exploring possibilities) with convergent thinking (evaluating feasibility), which is critical for driving 0 \-\>1 projects
- Extremely curious and passionate about working in GenAI applications space
Nice\-to\-Haves
- Experience training and/or serving ML models in production, or fine\-tuning LLMs for domain\-specific use cases
- Comfortable operating in privacy/PII\-sensitive environments and applying compliance mitigations
- Working knowledge of HTML, CSS, JavaScript, and modern frontend frameworks or libraries, with comfort building user\-facing experiences
Our mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven individuals who care deeply about making the financial ecosystem more equitable. We recognize that strong qualifications can come from both prior work experiences and lived experiences. We encourage you to apply to a role even if your experience doesn't fully match the job description. We are always looking for team members that will bring something unique to Plaid!
Plaid is proud to be an equal opportunity employer and values diversity at our company. We do not discriminate based on race, color, national origin, ethnicity, religion or religious belief, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, military or veteran status, disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state, and local laws. Plaid is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance with your application or interviews due to a disability, please let us know at [email protected].
Additional compensation in the form(s) of equity and/or commission are dependent on the position offered. Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k). Pay is based on factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience and skillset, and location. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans.
Salary Context
This $228K-$337K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $190K across 219 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 3,823 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Plaid, 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 $232,000 based on 797 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($282K) sits 22% above the category median. Disclosed range: $228K to $337K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Plaid AI Hiring
Plaid has 5 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer, AI Product Manager, Research Scientist. Based in San Francisco, CA, US. Compensation range: $223K - $367K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,168 tracked positions. That's 26% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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