Machine Learning Engineer

Our Applied ML team aims to reform how our users interact with 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 our users to sign up and navigate Stripe. We a...

As a machine learning engineer, you 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 lar...

As a machine learning engineer, you 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.
Our team operates fluidly and here are some problems you may tackle:
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?

And in the process you will:
Develop pipelines and automated processes to train and evaluate models in offline and online environments
Integrate ML models into production systems and ensure their scalability and reliability
Collaborate with product and strategy partners to propose, prioritize, and implement new product features
Engage with the latest developments in ML/AI and take calculated risks in transforming innovative ML ideas into productionized solutions

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.
Have at least 3 years of experience shipping ML systems in production
Hold yourself and others to a high bar when working with production systems
Take pride in taking ownership and driving projects to business impact
Thrive in a collaborative environment
5+ years of experience in full time software development roles
Experience shipping LLM integrations to user products with high quality
Experience operating in highly ambiguous environments
Knowledge about driving a hypothesis from data