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About This Role
Location
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New York, NY (HQ)
Employment Type
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Full time
Location Type
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Hybrid
Department
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Engineering
Compensation
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- $189K – $330K • Offers Equity
The final compensation will depend on the location and level at which the candidate is hired.
About Ramp
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Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $200B in annualized spend flows in and out of 70,000\+ companies: authorizing payments, flagging risk, categorizing spend, and closing books.
The problems are high\-stakes, data\-dense, and unforgiving.
We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome.
The median Ramp customer saves 5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same.
If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it.
About the Role
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As a software engineer on the AI Soltuions team, you will co\-lead customer engagements with an AI Solutions Strategist. The Strategist owns business discovery, ROI narrative, stakeholder alignment, and rollout planning. The engineer owns technical discovery, solution design, prototyping, implementation, and production readiness.
This is a deeply client\-facing role. You will spend significant time with customers and end users, moving projects from bootcamp and workflow discovery through implementation, launch, and steady production usage.
What You’ll Do
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- Translate customer goals into clear system requirements and non\-functional requirements covering security, privacy, reliability, performance, scalability, and cost.
- Partner directly with customers to understand current workflows, constraints, systems, data quality, and adoption blockers.
- Create and maintain solution architecture artifacts:
+ System context and data flow diagrams
+ Integration plan across Ramp and customer systems
+ Security model covering permissions, access patterns, and auditability
+ Evaluation plan covering quality metrics, acceptance tests, and red\-teaming
+ Operational plan covering monitoring, alerting, incident response, and runbooks
- Leverage core Ramp primitives to build efficiently, reusing existing product capabilities wherever possible.
- Prototype and validate workflows with end users to de\-risk the approach and prove product\-market fit.
- Drive projects from bootcamp and technical discovery through implementation, production launch, and operational handoff.
- Ensure deployed workflows are reliable, supportable, measurable, and adopted by customer teams.
- Convert deployments into reusable patterns, components, and playbooks for future AI Solutions projects.
What You Need
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- Experience shipping production software in high\-ownership environments.
- Ability to work directly with enterprise customers from discovery through production implementation.
- Experience in solutions architecture, technical consulting, forward deployed engineering, or pre\-sales engineering.
- Strong fundamentals in ML/GenAI, including problem decomposition, evaluation, and deployment trade\-offs.
- Strong coding ability in at least one of: Python, TypeScript/JavaScript, Java, Go, or similar.
- Ability to design secure, scalable systems and produce clear technical documentation.
- Comfort working across APIs, integrations, data pipelines, customer systems, and cloud infrastructure.
- Experience with cloud architecture on AWS, GCP, or Azure, and distributed systems patterns.
- Experience building LLM systems, including RAG, agents, monitoring, and evals.
- Willingness to travel up to \~75% as needed, flexible based on project needs and client needs
- Nice to have: Familiarity with finance operations workflows such as AP, procurement, expenses, close, reconciliation, and reporting.
Benefits available to all full\-time Ramp employees (Global)
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- Flexible PTO
- Centralized home\-office equipment ordering
- Health and wellness stipend
- Budget for intra\-office travel
- Weekly coffee stipend
United States
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- 100% medical, dental \& vision insurance coverage for you, with partial coverage for dependents
- One Medical annual membership
- 401(k), including employer match on contributions made while employed by Ramp
- Fertility HRA (up to $10,000 per year)
- Parental leave: up to 16 weeks (birthing \+ bonding) or 8 weeks (bonding only) at 100% pay
- Pet insurance
- In\-office perks: lunch, snacks, drinks, and more
- Relocation support to NYC or SF (as needed)
Canada
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- Group medical, dental, and vision coverage through Sun Life
- Life, AD\&D, and disability coverage
- Fertility drug coverage (up to $4,000 lifetime)
- Group Retirement Plan with employer match (RRSP \+ DPSP)
- Parental leave: up to 16 weeks (birthing \+ bonding) or 8 weeks (bonding only) at 100% pay, with additional time available at reduced pay
- Employee Assistance Program and virtual care through Lumino Health
United Kingdom
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- Private medical insurance through Freedom Elite
- Virtual GP and at\-home care via eMed x Livi
- Workplace pension through Penfold, with salary sacrifice option
- Parental leave: up to 16 weeks (birthing \+ bonding) or 8 weeks (bonding only) at 100% pay with additional time available at reduced pay
Referral Instructions
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If you are being referred for the role, please contact that person to apply on your behalf.
Other notices
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Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
*Beware of recruiting scams: Ramp will only contact you through official @**Ramp.com* *email addresses and will never ask for payment or sensitive personal information during the hiring process.*
Salary Context
This $189K-$330K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $185K across 231 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 4,317 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Ramp, 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. This role's midpoint ($259K) sits 19% above the category median. Disclosed range: $189K to $330K.
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.
Ramp AI Hiring
Ramp has 3 open AI roles right now. They're hiring across Data Scientist, AI Product Manager, AI Software Engineer. Positions span New York, NY, US, US. Compensation range: $297K - $330K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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.
Frequently Asked Questions
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