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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 DepartmentsEPD in S\&M
Compensation
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- Zone 1The target base salary for this position ranges from $216,000/year to $367,200/year in Zone 1\. 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. 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. $216K – $367\.2K
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.
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 lead this newly formed team that is dedicated to 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.
Responsibilities
- You will lead a team of 4 engineers, ranging from junior to staff, developing them through clear goal setting, coaching, and feedback
- You will define and drive the long\-term strategy and manage execution on the team, in close partnership with technical and product leaders across Plaid
- 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.
You will work with your team to:
- 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
- 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.
Qualifications
Must\-haves:
- 8\+ years of industry experience, that includes time as a Staff\-level engineer before transitioning into management.
- 1\+ years of engineering management experience
- 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
- Ability to deeply understand customer and user needs through user research and rapid experimentation \- be your own technical PM
- Track record of building and growing high performing engineering teams (either as an engineer or a manager).
- 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 have:
- 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
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 $216K-$367K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 roles with salary data).
View full AI/ML Engineer salary data →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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Plaid, 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 $185,000 based on 13,200 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($291K) sits 58% above the category median. Disclosed range: $216K to $367K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Plaid AI Hiring
Plaid has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Research Scientist, AI Software Engineer. Based in San Francisco, CA, US. Compensation range: $223K - $367K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,258 tracked positions. That's 26% above the national 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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