Machine Learning Engineer Applied ML
Machine Learning Engineer Applied ML
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Job Description
Who we are
About Stripe
Stripe is a financial infrastructure platform for businesses. Millions of companiesfrom the worlds largest enterprises to the most ambitious startupsuse 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 everyones reach while doing the most important work of your career.
About the team
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 are using the latest LLMs as well as finetuning our own models. Were an endtoend team going from ideas to models to shipping in production. You can learn more about our teams work from this recent talk.
What youll do
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.
Responsibilities
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 doesnt degrade online
- Does finetuning an LLM give us better performance
- What are the right OSS and inhouse platforms we should invest in
Who you are
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
- 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
Preferred qualifications
- 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
Employment Type
Full Time
