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Ripple

Staff Software Engineer, AI Platform

Ripple is hiring a Staff Software Engineer, AI Platform in Chicago, United States. It pays $208k-$260k a year and Level rates it ; you can apply on Ripple.

Level's read on this job

  • On Level's scale, this software engineer role is Builds AI ●●●●: the job is building AI systems.
  • Pay on this posting starts 25% higher than the typical software engineer posting on Level.
  • The posting mentions OpenAI and Azure OpenAI among its tools and skills.
  • Level classes it as a lead role.
  • Ripple has 111 other open roles on Level.

AI in this role

OpenAIAzure OpenAI

At Ripple, we’re building a world where value moves like information does today. It’s big, it’s bold, and we’re already doing it. Through our crypto solutions for financial institutions, businesses, governments and developers, we are improving the global financial system and creating greater economic fairness and opportunity for more people, in more places around the world. And we get to do the best work of our career and grow our skills surrounded by colleagues who have our backs.

If you’re ready to see your impact and unlock incredible career growth opportunities, join us, and build real world value.

The Opportunity

The mission, should you choose to accept it, is to pioneer and scale GSmart, Ripple Treasury's AI platform, into the future. If you enjoy solving complex business problems in a fast-paced, collaborative, and iterative environment this might be the right role for you!

We are seeking an innovative and forward-thinking Staff Software Engineer passionate about building a world-class AI platform through a world-class team. You will set the technical direction for how AI is built, measured, and operated across our enterprise treasury products, and stay hands-on in design, implementation, and production.

About the AI Platform Team

The AI Platform team builds and operates GSmart, the production AI platform behind the intelligent features in Ripple Treasury. Enterprise treasury teams in 160+ countries rely on it for forecasting, risk, onboarding, and analyst experiences.

GSmart is middleware, not a single feature. It gives every product team model routing, guardrails, observability, evaluation, and context injection out of the box, so each new AI capability is cheaper and safer to build than the last.

We're now extending the platform into an agentic layer: agents that reason over treasury data and act on behalf of finance teams, with the auditability that regulated financial workflows demand. We hold a high bar on measurement. If we can't evaluate it, we don't ship it.

What You'll Do – Be a Force Multiplier

Technical Leadership & Architecture:

  • Drive architectural decisions for the AI platform, balancing scalability, maintainability, cost, and rapid delivery
  • Establish the standards and design patterns for how teams build on GSmart: context assembly, prompt and model management, guardrails, and agent orchestration
  • Lead technical design reviews and give constructive feedback that elevates the team's architectural thinking
  • Identify and retire technical debt strategically, with roadmaps that improve both developer velocity and platform reliability

Evaluation & AI Quality:

  • Own the evaluation strategy for the platform: datasets, rubrics, automated and human review, and regression gates in CI/CD
  • Make AI quality measurable, so every prompt, model, or context change ships with evidence rather than opinion
  • Diagnose failure modes in production LLM systems and turn them into evals that prevent recurrence

Team Enablement & Mentorship:

  • Mentor mid-level and senior engineers, helping them grow into technical leadership roles
  • Break down complex, ambiguous problems into actionable work that distributes effectively across the team
  • Enable product teams to build on the platform and own their deliverables, through clear interfaces, documentation, and reference implementations
  • Champion engineering practices including observability, incident response, and production excellence

Strategic Technical Vision:

  • Anticipate scalability, latency, and cost challenges before they become blockers (e.g., 10x growth in inference volume or expansion into new data-residency regions)
  • Evaluate emerging models, frameworks, and agent patterns, and recommend when and how to adopt them
  • Balance technical excellence with pragmatic delivery, knowing when "good enough now" beats "perfect later"

Ownership & Accountability:

  • Own critical platform systems end-to-end, from design through production support
  • Take responsibility for production reliability and proactively prevent incidents
  • Step into ambiguous situations and drive them to resolution without needing explicit direction
  • Lead by example in code quality, documentation, and operational excellence
  • Make tough technical trade-off decisions and clearly communicate the reasoning

What You'll Work On

  • AI middleware — the shared services every AI feature runs through: model routing across providers, guardrails, memory, and context injection
  • Evaluation and observability — tracing, evals, prompt registry, experimentation, and cost tracking that make AI behavior visible and auditable
  • Agentic layer — the orchestration, tooling, and safety model for agents that work across treasury data and workflows
  • Regional deployment — infrastructure-as-code that brings inference and memory into new geographies while meeting data-sovereignty requirements
  • Product integration — partnering with Forecasting, Risk, Liquidity, and Digital Assets teams to turn customer problems into platform capabilities

Essential Knowledge / Experience

Years & Scope:

  • 8+ years of professional software engineering experience
  • 3+ years working on complex, business-critical systems in production
  • 2+ years building and operating LLM-based systems in production, beyond proof of concept
  • Strong communication skills with experience working in cross-functional projects
  • Track record of delivering projects that span multiple quarters and require coordination across teams

Technical Leadership:

  • Led technical designs that were implemented by teams of 3+ engineers
  • Made architectural decisions that had lasting impact on system capabilities
  • Established technical patterns that were adopted across teams

AI Platform Engineering:

  • Has owned a production LLM platform or shared AI service used by multiple product teams
  • Can define and run evaluations: golden datasets, rubric and model-graded scoring, regression testing, and online monitoring
  • Understands context engineering and retrieval, and can explain when each is the right tool
  • Hands-on with model gateways and routing, prompt management, guardrails, and LLM observability (we use Azure OpenAI, LiteLLM, and Langfuse)
  • Experience designing agentic systems, including tool use, orchestration, memory, and failure handling
  • Speaks fluently about failure modes, cost, and latency trade-offs, not just wins

Cloud & Infrastructure:

  • Proficiency with at least one major cloud platform (preferably Azure)
  • Experience with containerized workloads (Docker; Azure Container Apps or Kubernetes)
  • Understanding of infrastructure as code (Terraform) and CI/CD pipeline design
  • Knowledge of observability: logging, metrics, tracing, and alerting strategies

Core Technologies:

  • Strong full-stack skills, front-end through back-end and APIs, in a modern stack such as C#/.NET and TypeScript/Node.js
  • Deep experience building large-scale, multi-tenant enterprise applications
  • Experience building and maintaining RESTful APIs, and knowing when to use alternative patterns (streaming, gRPC, webhooks)
  • Working knowledge of data stores used in AI systems, including relational databases and caches such as Redis

Nice to Have:

  • Experience in fintech, treasury, banking, or another regulated domain
  • Experience with data-residency or multi-region AI deployments
  • Familiarity with digital assets

What You Will Get

  • A high impact, high visibility role at a growing SaaS company that values personal growth, accountability, and the concept of “good work”
  • This is a great opportunity for someone who wants to make a big impact, work in a fast-paced and collaborative environment, and win as a team to scale a growing business
  • A culture of open collaboration and problem solving
  • An empowered role on the Engineering team, responsible for the platform every AI feature in Ripple Treasury is built on
  • Great benefits, and culture

For positions that will be based in IL, the annual salary range for this position is below. Actual salaries may vary based on numerous factors including, among other things, an individual applicant’s experience and qualifications for the position. This range does not include equity or additional compensation, such as bonuses or commissions.

IL Annual Base Salary Range$208,000—$260,000 USD

WHO WE ARE:

Do Your Best Work

  • The opportunity to build in a fast-paced start-up environment with experienced industry leaders
  • A learning environment where you can dive deep into the latest technologies and make an impact. A professional development budget to support other modes of learning.
  • Thrive in an environment where no matter what race, ethnicity, gender, origin, or culture they identify with, every employee is a respected, valued, and empowered part of the team.
  • In-office collaboration for moments that matter is important to our culture, and we give managers and teams the flexibility to decide which 10+ days a month they come in.
  • Bi-weekly all-company meeting - business updates and ask me anything style discussion with our Leadership Team
  • We come together for moments that matter which include team offsites, team bonding activities, happy hours and more!

Take Control of Your Finances

  • Competitive salary, bonuses, and equity
  • Competitive benefits that cover physical and mental healthcare, retirement, family forming, and family support
  • Employee giving match
  • Mobile phone stipend

Take Care of Yourself

  • R&R days so you can rest and recharge
  • Generous wellness reimbursement and weekly onsite & virtual programming
  • Generous vacation policy - work with your manager to take time off when you need it
  • Industry-leading parental leave policies. Family planning benefits.
  • Catered lunches, fully-stocked kitchens with premium snacks/beverages, and plenty of fun events

Benefits listed above are for full-time employees.

Ripple is an Equal Opportunity Employer. We’re committed to building a diverse and inclusive team. We do not discriminate against qualified employees or applicants because of race, color, religion, gender identity, sex, sexual identity, pregnancy, national origin, ancestry, citizenship, age, marital status, physical disability, mental disability, medical condition, military status, or any other characteristic protected by local law or ordinance.
Please find our California, UK/EU, Singapore, DIFC, ADGM, Australia, and Switzerland Applicant Privacy Notice for reference.

How we rate this

Staff Software Engineer, AI Platform at Ripple rates 86 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

OpenAIAzure OpenAI

Also common in software engineer jobs at this level

Pay

This job starts at
$208k
Typical software engineer job
$167k

This posting starts 25% above the typical software engineer job. See software engineer jobs

Questions you could be asked

  1. What's a project where you used OpenAI hands-on?
  2. Walk me through how you've used Azure OpenAI in your day-to-day work.
  3. How would you decide a model or AI system is ready to ship?
  4. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

  • List these exact terms on your resume: OpenAI and Azure OpenAI. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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