Staff Machine Learning Engineer, Applied ML Accelerator - San Francisco, CA, United States - Stripe

Description

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.

Some examples include helping our users resolve issues with Stripe faster or making it easier for data scientists to write SQL queries.

We're an end-to-end team going from ideas to models to shipping in production.

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.

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?

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.

At least 5 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.

For sales roles, the range provided is the role's On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

medical, dental, and vision benefits;