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NEXTMOVEFDE careers · United States

Staff Software Engineer, Machine Learning Platform

No longer in the current catalog. Last included 2026-09-09. Check the employer’s posting for availability.

Technologies

ML Platform · low-latency model inference · feature stores · real-time monitoring · LLM/agent orchestration · service-oriented architecture · distributed systems · AWS · SageMaker · Bedrock · Databricks · OpenAI

About Stripe

Financial infrastructure APIs (payments, billing, tax, identity, issuing) used by millions of businesses from startups to global enterprises to run revenue online.

Private Late

Job description

The full responsibilities and requirements are on the employer’s site.

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Source and classification

Internal deployment & tooling · Evidence for this classification:

etc.), we have lots of exciting opportunities to innovate in ML Platform at Stripe. The ML Platform team builds the platforms and services that enable ML engineers and data scientists across Stripe to take data and build features and models from prototype to production—reliably, at low latency, and at scale. Our scope spans ML training infrastructure, model serving and deployment, feature computation and online serving, observability and monitoring, and agentic AI capabilities. We work closely with product teams, data scientists, and platform infrastructure teams to build powerful, flexible, and user-friendly systems that substantially increase ML velocity across the company. What you'll do You'll serve as a technical lead across the ML Platform space and a key contributor to the evolution of the platforms that power Stripe's ML-driven products. As a Staff Engineer, you'll make
More from the job description

Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. About the team Stripe processes over $1.9T in payments volume per year, which is roughly 1.6% of the world's GDP, for millions of customers from startups to enterprises. The tremendous amount of data makes Stripe one of the best places to do machine learning. While being an integral part of almost every product line at Stripe (e.g., Payments, Radar, Capital, Billing, etc.), we have lots of exciting opportunities to innovate in ML Platform at Stripe. The ML Platform team builds the platforms and services that enable ML engineers and data scientists across Stripe to take data and build features and models from prototype to production—reliably, at low latency, and at scale. Our scope spans ML training infrastructure, model serving and deployment, feature computation and online serving, observability and monitoring, and agentic AI capabilities. We work closely with p [... source excerpt omitted ...] teams, data scientists, and platform infrastructure teams to build powerful, flexible, and user-friendly systems that substantially increase ML velocity across the company. What you'll do You'll serve as a technical lead across the ML Platform space and a key contributor to the evolution of the platforms that power Stripe's ML-driven products. As a Staff Engineer, you'll make decisions with a large impact on Stripe. You'll influence our investments and strategy while making our systems more reliable, secure, and a delight to use. You'll work cross-functionally with other technical staff, data science, product, and senior leadership to increase the impact of ML at Stripe. Yo [... source excerpt omitted ...] hape platform and product roadmap, combining technical excellence with creative problem-solving. Scope and lead large projects with significant business impact, driving them from requirements through design, implementation, and production operation. Work with ML engineers, data scientists, and product teams directly to translate their needs into functional requirements and scalable technical solutions. Arbitrate critical decisions that balance competing priorities while meeting latency, reliability, cost, and security constraints. Serve as a key engineering representative, engaging senior leaders across Stripe and advising the leadership team on key technical considerations relat

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