Ramp에서 Applied AI Engineer를 채용합니다. 풀스택 개발 역량과 LLM을 활용한 프로덕션 환경의 AI 프로젝트 경험이 필수입니다. AI 인프라 구축 및 모델 파인튜닝을 통해 금융 플랫폼의 혁신을 주도할 인재를 찾습니다. 엔드 투 엔드로 문제를 해결하며 성장하고자 하는 분들의 많은 지원 바랍니다.
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.
The Applied AI team at Ramp is at the forefront of leveraging AI to drive innovation across our platform. We are seeking strong full-stack engineers who are proficient in web frameworks, backend development, and infrastructure. You will work on exciting projects such as AI Agents, Retrieval-Augmented Generation, Structured Extraction (we made https://github.com/1rgs/jsonformer), internal tooling for customer-facing teams, fine-tuning models, and build infrastructure for LLM inference. If you're passionate about working on real production use cases of large language models (LLMs) and want to contribute to groundbreaking AI applications, this role is for you.
Ship full-stack AI projects end to end
Build and integrate components for AI infrastructure, supporting production-level inference and fine-tuning
Develop and improve engineering processes, tools, and systems to scale AI solutions across Ramp
Create tools and internal platforms to enhance the productivity and capabilities of Ramp's AI and engineering teams
Proficiency in full-stack development, with a strong understanding of web frameworks, backend systems, and cloud infrastructure
A track record of working on full-stack AI projects, particularly those involving production use cases of LLMs
Experience building backend systems and infrastructure that can support AI-driven products
Flexible PTO
Unlimited AI token usage
Centralized home-office equipment ordering
Health and wellness stipend
Budget for intra-office travel
Weekly coffee stipend
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)
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
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
If you are being referred for the role, please contact that person to apply on your behalf.
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.
