Machine Learning Engineer

at Stripe

USD 212,000-318,000 per year

MIDDLE

✅ Hybrid

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI Algorithms @ 3 Data Pipelines @ 3 Deep Learning @ 3 Machine Learning @ 3 Mathematics @ 3 PyTorch @ 3 Reinforcement Learning @ 3 Spark @ 3 Statistics @ 3 TensorFlow @ 3 XGBoost @ 3

Details

Stripe is a financial infrastructure platform for businesses. The role focuses on building machine learning models and large-scale systems for underwriting and portfolio management for Stripe Capital.

Responsibilities

Design state-of-the-art machine learning models and large-scale ML systems based on ML principles, domain knowledge, risk, regulatory, and engineering constraints.
Design systems to accelerate the time from idea to deployment of new models.
Experiment with and iterate on ML models using tools including PyTorch and TensorFlow to achieve business goals and improve efficiency.
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 and AI and transform innovative ideas into productionized solutions.

Requirements

Bachelor's degree or foreign equivalent in Computer Science, Machine Learning, Mathematics, Physics, Statistics, or a related field.
At least two years of experience building and shipping ML systems in production.
At least two years of experience with ML algorithms and model architectures; designing, training, and evaluating machine learning models; productionizing and deploying ML models at scale; orchestrating data pipelines and leveraging large-scale datasets; and building and deploying ML models to solve business problems.
At least one year of experience with ML libraries and frameworks including PyTorch, TensorFlow, XGBoost, or Spark.
At least one year of experience with deep learning, including transformers, test-time compute, or reinforcement learning.

Benefits

Additional benefits may include equity, a company bonus or sales commissions/bonuses, a 401(k) plan, medical, dental, and vision benefits, and wellness stipends.

The role permits 50% telecommuting. Multiple positions are available.