Staff Machine Learning Engineer, Financial Products

United States Digital Space LLC; San Francisco (CA); On-site; USD 297,000 - 401,000; Full time

Benefits offered by this job

RSUs

Job summary

the company is building a Machine Learning Engineering team in San Francisco focused on Credit Risk Modeling for Underwriting within Financial Products. This team will develop the models, scorecards, and production systems that enable the company to scale its credit products 100x.

As a Staff Machine Learning Engineer you will design, productionize, and operate machine learning models and rule-based decision systems that power credit products.

Qualifications

• 8+ years of machine learning engineering experience.
• Strong Python programming and Java experience.
• End-to-end ML model lifecycle in production.
• Experience processing big data to feed models.
• Understanding of software engineering and MLOps.
• Knowledge of data science, statistics, ML techniques.
• Familiarity with Python data science tools.
• Experience with ML infrastructure (K8s, Docker, Airflow).
• Ability to communicate complex results to diverse audiences.

Responsibilities

• Develop and maintain scalable production ML pipelines for feature engineering, model training, validation, and deployment.
• Identify and fix performance bottlenecks in ML training and inference (memory consumption, online latency, training time).
• Collaborate with software engineers to integrate ML solutions into products and services.
• Collaborate with CreditOps and data teams to integrate effectively with current tools, and shape priority for future tools.
• Support and encourage good engineering practices on product ML teams.

Skills

Python

Java

ML lifecycle in production

Big data pipelines

MLOps

Data science concepts

PySpark / Spark

TensorFlow / PyTorch

XGBoost / LightGBM

Pandas

MLFlow

Airflow

Kubernetes / Docker

Stakeholder communication

Tools

PySpark

SQL

TensorFlow

PyTorch

XGBoost

LightGBM

Pandas

MLFlow

Airflow

Kubernetes

Docker

Job description

This is the company.

the company provides payments, data, and financial products in a single solution for customers like Meta, Uber, H&M, and Microsoft - making us the financial technology platform of choice. At the company, everything we do is engineered for ambition.

For our teams, we create an environment with opportunities for our people to succeed, backed by the culture and support to ensure they are enabled to truly own their careers. We are motivated individuals who tackle unique technical challenges at scale and solve them as a team. Together, we deliver innovative and ethical solutions that help businesses achieve their ambitions faster.

Financial ProductsAbout the Role

The Financial Products org at the company is at the forefront of our evolution, building the foundational infrastructure that enables our customers to manage their finances, issue cards, and access credits and financing globally. the company is building a Machine Learning Engineering team in San Francisco focused on Credit Risk Modeling for Underwriting within Financial Products. This team will develop the models, scorecards, and production systems that enable the company to scale its credit products 100x.

As a Staff Machine Learning Engineer you will design, productionize, and operate machine learning models and rule-based decision systems that power credit products. You will work across the full model lifecycle, from research and data analysis to training, deployment, monitoring, and continuous improvement.

This role is ideal for an engineer who combines strong machine learning and production engineering experience with sound judgment in high-integrity financial systems. You will help build continuous data flywheels that improve underwriting decisions while balancing rapid product innovation with robustness, explainability, and global scale.

We are looking for engineers with a customer-problem-first mindset and experience building reliable ML systems in production. You will work closely with product, engineering, risk, and data teams to deliver underwriting capabilities for some of the world’s leading businesses.

In this role, you will:

• Develop and maintain scalable production ML pipelines for feature engineering, model training, validation, and deployment. Examples ML domains are: supervised and semi-supervised learning methods for inference on credit risk patterns;
• Identify and fix performance bottlenecks in ML training and inference (memory consumption, online latency, training time etc.);
• Collaborate with software engineers to integrate ML solutions into products and services;
• Collaborate with CreditOps and data teams to integrate effectively with current tools, and shape priority for future tools;
• Support and encourage good engineering practices on product ML teams;

Who You Are:

• You have 8+ years of experience as an engineer working in the machine learning domain;
• You are a strong Python programmer and you have experience in Java.
• You have experience with the full machine learning model lifecycle in production flows;
• You have experience leveraging big data to create the pipelines needed to feed the models with appropriate data;
• You have a strong understanding of good software engineering practices as well as data engineering and MLOps principles;
• You have knowledge of data science, statistics and machine learning techniques;
• You have strong familiarity with the standard data science toolkit in python, such as (py)spark, (Trino) SQL, Tensorflow, PyTorch, XGBoost/LightGBM, Pandas, MLFlow or similar MLOps frameworks, and Airflow;
• You have knowledge/experience of working with ML infrastructure components with tools such as k8s, docker, airflow, argo-workflows, prometheus, grafana
• You have an experimental mindset with a launch fast and iterate mentality;
• You proactively take the lead in projects, from ideation to deployment. You have experience working with a wide range of stakeholders and can clearly communicate complex outcomes over a wide range of audiences.

Nice to Have:

• You have experience on underwriting models or systems
• You have experience working with a Machine Learning ‘Feature Store’

Diversity, Equity, and Inclusion at the company

We actively seek different perspectives to sharpen our ideas. Our unique culture is a product of our diverse backgrounds and experiences. No matter who you are or where you’re from, you’re welcome to be your true self at the company.

If you need accommodations or support during the recruitment process to perform at your best, you’ll have the opportunity to let our team know in your application. We encourage people from all backgrounds and abilities to apply.

Why the company?

This is an exceptional opportunity to join a solid, established company with a startup mindset, characterized by small teams, direct communication, and work on important challenges. You’ll have direct access to massive global datasets (e.g., payments, identity data) and the ability to see your team's work have an immediate impact at scale. We offer an environment of ownership and speed, where a focused research team can make a significant difference. You will be at the forefront of bridging ML and fintech, shaping how these two critical areas intersect. We are looking for individuals who are eager to stay connected to the edge of ML innovation while ensuring we deliver practical value to our global platform.

While this is a San Francisco-based role, we recognize that global mobility matters— especially for candidates navigating complex situations. the company has deep roots in Europe and a strong global presence, and we’re open to future relocation conversations to our offices in Amsterdam or Madrid if that better supports your personal or professional goals in the future. Our aim is to build a lasting foundation for you at the company—starting in San Francisco, but with the flexibility to adapt as life evolves.

The annual base salary range for this role is $297,000 - $401,000, plus RSUs; to learn more about our compensation philosophy,

This position is based out of the San Francisco office.

Our Diversity, Equity and Inclusion commitments

Our unique approach is a product of our diverse perspectives. This diversity of backgrounds and cultures is essential in helping us maintain our momentum. Our business and technical challenges are unique, and we need as many different voices as possible to join us in solving them - voices like yours. No matter who you are or where you’re from, we welcome you to be your true self at the company.

Studies show that women and members of underrepresented communities apply for jobs only if they meet 100% of the qualifications. Does this sound like you? If so, the company encourages you to reconsider and apply. We look forward to your application!

What’s next?

Ensuring a smooth and enjoyable candidate experience is critical for us. We aim to get back to you regarding your application within 5 business days. Our interview process tends to take about 4 weeks to complete, but may fluctuate depending on the role. Learn more about our hiring process here. Don’t be afraid to let us know if you need more flexibility.

the company is an equal