Search for Machine Learning Engineer roles in San Francisco, focusing on financial services, requiring 6+ years of experience, with proficiency in PyTorch and TensorFlow.

Okay, I've searched for Machine Learning Engineer roles in San Francisco within the financial services industry, requiring 6+ years of experience and proficiency in PyTorch and TensorFlow. I have found 57 positions for you.

Machine Learning Engineer

San Francisco, CA

Financial Services

6 years + Experience

PyTorch

TensorFlow

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 hired to design, build, and deploy advanced machine learning models that mitigate credit risk while ensuring an optimal user experience. This role goes beyond simply coding; it requires a deep understanding of the intersection between machine learning, product strategy, and risk management. The primary responsibilities include collaborating with cross-functional teams to develop scalable ML systems that are critical to maintaining Stripe's financial integrity and enhancing product offerings. Key challenges will involve navigating ambiguity in model performance and business objectives, while also innovating within an evolving technical landscape. Success in this role would mean not only delivering robust ML models but doing so in a manner that aligns tightly with Stripe’s long-term goals, ultimately lowering credit risk and enhancing the company’s financial health.

Company Analysis:

Stripe operates in the competitive landscape of financial technology, providing essential infrastructure for businesses to facilitate online transactions. As a market leader aiming to increase the GDP of the internet, Stripe prioritizes innovation and agility, which influences its hiring needs and internal culture. The company's fast-paced, collaborative environment values creativity and proactive problem-solving—qualities essential for a Machine Learning Engineer. Within this context, the Credit Detection team will play a pivotal role in shaping risk management strategies and aligning with broader company objectives. The role signifies a blend of technical proficiency and strategic influence, contributing directly to Stripe’s ability to manage risk while supporting user-centric product experiences. This strategic alignment emphasizes the importance of this position not just as a technical job, but as a key contributor to the organization's growth.

Mock up some interview questions based on the job description.

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

Behavioral Questions:
1. Tell me about a time when you had to develop a machine learning model that addressed a significant business challenge. What was your approach and the outcome?
1. Describe an instance where you collaborated with cross-functional teams. How did you ensure effective communication and alignment on project goals?
1. Can you provide an example of a time when you faced ambiguity in a project? How did you navigate it and what was the result?
1. Share an experience where your machine learning implementation had a direct impact on mitigating risks within a project or company.
Technical/Role-Specific Questions:
1. How would you approach the design of a large-scale ML system for credit risk detection? What key factors would you consider?
1. Discuss your experience with specific ML libraries such as PyTorch or TensorFlow. Can you give an example of a project where you used one of these tools?
1. Explain your process for developing data pipelines for machine learning models. What challenges have you faced and how did you overcome them?
1. What methodologies do you prefer when evaluating the performance of machine learning models, particularly in terms of scalability?
Situational Questions:
1. Imagine you're working on improving an existing credit risk model that is underperforming. What steps would you take to identify and address the issues?
1. You’ve been tasked with integrating a new ML model into a production system. What are your key considerations to ensure its reliability and scalability?
Company Fit/Motivation Questions:
1. Why are you interested in joining Stripe specifically, and how do you think your skills align with our mission?
1. How do you feel Stripe’s values resonate with your own professional values and work style?
Questions for the Candidate to Ask the Interviewer:
1. What are the current challenges the Credit Detection team is facing, and how can someone in this role contribute to overcoming them?
1. Can you share more about the collaborative dynamics between the Machine Learning team and other departments?
1. How does Stripe’s culture support professional development and innovation in technology, especially within the ML space?

Job Details

Overview Company

Senior Machine Learning Engineer

$212k/y-$318k/y

Stripe

6 days ago

Highlight

HybridFull-time6+ yearsSenior

Summary

Stripe is seeking a Machine Learning Engineer, Credit Risk to join their team in San Francisco, CA. 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.

San Francisco, CA