Applied AI Engineer

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

• 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 expense coverage to NYC or SF (if 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.

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