Search for Machine Learning Engineer roles in Seattle, focused on credit risk analysis, with 6+ years of experience, using PyTorch or TensorFlow, and remote work options.

Okay, I've searched for Machine Learning Engineer roles in Seattle that focus on credit risk analysis, requiring 6+ years of experience, and utilizing PyTorch or TensorFlow, with options for remote work. I found 45 positions that match your criteria.

Machine Learning Engineer

Seattle, WA

6 years + Experience

PyTorch

TensorFlow

Remote

Can you help me to analyze the job description and company culture?

Sure. Here's the analysis:

Job Analysis:

The Machine Learning Engineer for Credit Risk at Stripe is fundamentally tasked with mitigating credit risks through the development and deployment of state-of-the-art machine learning models. This role requires not only technical proficiency in ML and data science but also strategic collaboration across multiple teams, including product management and operations. As such, the candidate will need to navigate complex interdisciplinary communication and prioritize initiatives that align with business objectives while transferring innovative ideas into practical applications. Success in this role is marked by the ability to integrate these ML models into Stripe’s production systems effectively, ensuring scalability and reliability. Over the first 6-12 months, success will look like the enhancement of the existing risk mitigation strategies resulting in a reduction of credit losses, while maintaining a seamless user experience—a critical balance for Stripe's mission of boosting the internet's GDP. Therefore, proactive problem-solving and initiative are essential, as the candidate will face ongoing uncertainties inherent in financial technology and risk management.

Company Analysis:

Stripe operates in the financial technology sector, positioning itself as a leader in payment infrastructure for businesses of all sizes. Its market presence is characterized by a commitment to innovation and accessibility, making it a viable partner for both enterprises and startups. This strategic focus on increasing the GDP of the internet aligns directly with the Credit Risk team's objectives; successfully managing credit risk is essential for sustainable growth and ensures Stripe's competitive advantage in a rapidly evolving industry. The company culture at Stripe is likely fast-paced, collaborative, and driven by a mission-oriented mindset, favoring those who thrive on initiative and teamwork. The role of the Machine Learning Engineer fits within a well-defined structure of cross-functional teams that contribute to the overarching goals of business sustainability and technological innovation. By engaging with complex ML-related challenges, this role not only reinforces Stripe's immediate objectives but also contributes to its long-term strategic vision of transforming how financial systems function on a global scale.

Mock up some interview questions based on the job description.

Absolutely. Here are some mock interview questions that could come up:

Can you describe a time when you developed a machine learning model that significantly impacted business decision-making?
Tell me about a challenge you faced while integrating machine learning models into a production environment and how you overcame it.
How do you prioritize tasks when working on multiple projects with tight deadlines? Can you provide an example?
Describe your experience with any adversarial domains, such as Payments or Fraud detection. How did you approach problem-solving in that context?
What methodologies do you employ when iterating on machine learning models to ensure they meet business goals?
Can you explain how you would approach designing a new ML model for credit risk detection based on Stripe's business model?
How do you ensure the reliability and scalability of machine learning systems you deploy? Could you walk through your process?
Imagine you are faced with conflicting priorities from your product and data science teams. How would you navigate this situation?
Why do you want to work at Stripe specifically, and how do you see your values aligning with our mission?
What excites you most about the potential impact of machine learning technologies on financial services?
What strategies would you use to stay updated with the latest developments in the ML/AI field, and how would you apply that knowledge at Stripe?
Is there a particular project you would hope to tackle in your first year at Stripe? Why is that important to you?
How do you see the role of machine learning evolving in the context of credit risk within financial technology?
What questions do you have about the team dynamics within the Credit Detection team at Stripe?
How would you describe your ideal work environment, and how does this align with Stripe's hybrid work model?

Job Details

Overview Company

Senior Machine Learning Engineer

$212k/y-$318k/y

Stripe

5 days ago

Highlight

HybridFull-time6+ yearsSenior

Summary

Stripe is seeking a Machine Learning Engineer, Credit Risk to join their team in Seattle, WA. The role involves designing, building, training, evaluating, deploying, and owning ML models in production to manage credit risk at scale.

About the Role

As a Machine Learning Engineer, Credit Risk, you will be responsible for designing state-of-the-art ML models and large-scale ML systems for detection and decisioning for Stripe products. You will experiment and iterate on ML models using tools such as PyTorch, TensorFlow, and XGBoost to achieve key business goals. You will develop pipelines and automated processes to train and evaluate models in offline and online environments, integrate ML models into production systems, and collaborate with product and strategy partners to propose, prioritize, and implement new product features.

About You

Required:

6+ years of industry experience building and shipping ML systems in production.
Proficient with ML libraries and frameworks such as PyTorch, TensorFlow, XGBoost, as well as Spark.
Knowledge of various ML algorithms and model architectures.
Hands-on experience in designing, training, and evaluating machine learning models.
Hands-on experience in orchestrating complicated data pipelines and efficiently leveraging large-scale datasets.

Preferred:

MS/PhD degree in ML/AI or related field (e.g. math, physics, statistics).
Experience with DNNs including the latest architectures such as transformers and LLMs.
Experience working in Java or Ruby codebases.
Proven track record of building and deploying ML systems that have effectively solved ambiguous business problems.
Experience in adversarial domains such as Payments, Fraud, Trust, or Safety.

Benefits

Equity, company bonus or sales commissions/bonuses.
401(k) plan.
Medical, dental, and vision benefits.
Wellness stipends.

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. Headquartered in San Francisco and Dublin, the company aims to increase the GDP of the internet.

Seattle, WA