Overview
On Site
Full Time
Skills
Machine Learning (ML)
Internet
SQL
Computer science
Finance
Startups
Payments
GDP
Productivity
Automation
Data
Shipping
Training
Algorithms
Operations support systems
Streaming
Statistics
Physics
Python
Scala
Apache Spark
Metrics
Collaboration
Job Details
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
The newly formed Productivity ML Accelerator team aims to reform productivity across Stripe. We are doing so by (a) automating the easy tasks, and (b) assisting our users in the difficult tasks. Some examples include helping our users resolve issues with Stripe faster or making it easier for data scientists to write SQL queries. We are using the latest LLMs as well as fine-tuning our own models. We're an end-to-end team going from ideas to models to shipping in production.
What you'll do
We are looking for experienced Machine Learning Engineers who will be responsible for analyzing opportunities, proposing ideas, training & evaluating ML models, running experiments, and deploying everything to production. You will also have the opportunity to contribute to and influence ML architecture at Stripe as well as be a part of a larger ML community.
Responsibilities
Our team operates fluidly and here are some problems you may tackle:
We are looking for ML Engineers who are passionate about using ML to improve products and delight customers. You have experience developing streaming feature pipelines, building ML models, and deploying them to production, even if it involves making substantial changes to backend code. You are comfortable with ambiguity, love to take initiative, and have a bias towards action.
Minimum requirements
Hybrid work at Stripe
Office-assigned Stripes spend at least 50% of the time in a given month in their local office or with users. This hits a balance between bringing people together for in-person collaboration and learning from each other, while supporting flexibility about how to do this in a way that makes sense for individuals and their teams.
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
The newly formed Productivity ML Accelerator team aims to reform productivity across Stripe. We are doing so by (a) automating the easy tasks, and (b) assisting our users in the difficult tasks. Some examples include helping our users resolve issues with Stripe faster or making it easier for data scientists to write SQL queries. We are using the latest LLMs as well as fine-tuning our own models. We're an end-to-end team going from ideas to models to shipping in production.
What you'll do
We are looking for experienced Machine Learning Engineers who will be responsible for analyzing opportunities, proposing ideas, training & evaluating ML models, running experiments, and deploying everything to production. You will also have the opportunity to contribute to and influence ML architecture at Stripe as well as be a part of a larger ML community.
Responsibilities
Our team operates fluidly and here are some problems you may tackle:
- Which Text2SQL algorithm should we implement? What are the tradeoffs?
- How do we evaluate a system offline & online?
- How do we improve performance to match (and beat) humans?
- How do we ensure model quality doesn't degrade online?
- Does fine-tuning an LLM give us better performance?
- What are the right OSS and in-house platforms we should invest in?
We are looking for ML Engineers who are passionate about using ML to improve products and delight customers. You have experience developing streaming feature pipelines, building ML models, and deploying them to production, even if it involves making substantial changes to backend code. You are comfortable with ambiguity, love to take initiative, and have a bias towards action.
Minimum requirements
- At least 7 years of industry experience doing end-to-end ML development on a machine learning team and bringing ML models to production
- Advanced degree in a quantitative field (e.g. computer science, statistics, physics, ...)
- Proficient in Python, Scala, Spark
- Knowledge about how to manipulate data to perform analysis, including querying data, defining metrics, or slicing and dicing data to evaluate a hypothesis.
- Experience evaluating niche and upcoming ML solutions
Hybrid work at Stripe
Office-assigned Stripes spend at least 50% of the time in a given month in their local office or with users. This hits a balance between bringing people together for in-person collaboration and learning from each other, while supporting flexibility about how to do this in a way that makes sense for individuals and their teams.