Machine Learning Modeler, Advanced Insights & Modeling

Block; San Francisco (CA); On-site; USD 160,700 - 283,600; Full time

Job summary

A tech company in San Francisco seeks a Machine Learning Modeler to design models that support data-driven decision-making. The ideal candidate has 5+ years in ML engineering, proficiency in Python, and experience with tools like scikit-learn and Airflow. Responsibilities include developing ML pipelines and collaborating across departments to implement solutions that shape strategic decisions.

Qualifications

• 5+ years of experience in software or ML engineering with hands-on experience in ML systems.
• Deep understanding of applied ML and forecasting.
• Strong proficiency in Python and common ML libraries.

Responsibilities

• Design and implement forecasting models that power strategic decisions.
• Build end-to-end ML pipelines ensuring reproducibility.
• Collaborate with Data Science to productionize models.

Skills

Machine Learning

Python

Data Pipelines

Forecasting

Cross-Functional Collaboration

Tools

scikit-learn

XGBoost

Airflow

BigQuery

Spark

Job description

Machine Learning Modeler, Advanced Insights & Modeling

Join to apply for the Machine Learning Modeler, Advanced Insights & Modeling role at Block.

Block is one company built from many blocks, all united by the same purpose of economic empowerment. The foundational teams — People, Finance, Counsel, Hardware, Information Security, Platform Infrastructure Engineering, and more — provide support and guidance at the corporate level. They work across business groups and around the globe to develop inclusive People policies, forecast finances, give legal counsel, safeguard systems, nurture new initiatives, and more.

The Role

We're looking for a Machine Learning Modeler to design and scale the next generation of intelligent systems that power data‑driven decision‑making across Block—from Cash App and Square to corporate domains like Treasury, Cost, and Accounting. You'll build models and AI‑driven workflows that don't just predict outcomes—they help shape them. Working across the full ML lifecycle, you'll transform raw data into foresight through advanced modeling, agentic AI workflows, and automation frameworks that enable faster, smarter decisions at scale.

You’ll partner closely with analytics and data science teams to bring experimental models into production and with finance and operations partners to build explainable, self‑optimizing systems that make forecasts and insights transparent, actionable, and continuously learning.

You Will

• Design and implement forecasting, financial, or optimization models that power strategic decisions across Block.
• Build end‑to‑end ML pipelines for training, deployment, and monitoring, ensuring reproducibility and performance at scale.
• Collaborate with Data Science to productionize experimental models and integrate them into live systems.
• Partner with Analytics & Finance teams to ensure forecasts are interpretable, accurate, and aligned with business objectives.
• Develop or contribute to explainability tools that communicate model drivers, confidence, and uncertainty to stakeholders.
• Improve data pipelines and workflows using systems like Airflow, BigQuery, and Spark.
• Establish and document best practices for model evaluation, experimentation, and maintenance.
• Translate complex technical findings into clear, actionable recommendations for non‑technical partners.
• Contribute to a culture of curiosity, high‑quality engineering, and continuous learning within the Advanced Insights & Modeling organization.

You Have

• 5+ years of experience in software or ML engineering, with hands‑on experience delivering production‑grade ML systems.
• Deep understanding of applied ML and forecasting, including time‑series, regression, and value prediction modeling.
• Strong proficiency in Python and common ML libraries such as scikit‑learn, XGBoost, LightGBM, and NumPy/pandas.
• Experience building data pipelines using tools such as Airflow, Spark, or similar orchestration systems, and working with BigQuery or other large‑scale data warehouses.
• Familiarity with model explainability techniques (e.g., SHAP, feature attribution, uncertainty quantification).
• Experience connecting model design to business objectives.
• Proven ability to work cross‑functionally and drive high‑impact results.
• Experience in forecasting or planning models in fintech, consumer, or marketplace settings.
• Exposure to automated model serving, monitoring, or feedback loops in production.
• Background in statistical modeling, uncertainty estimation, or model interpretability research.
• A passion for transforming complex ML outputs into actionable insights and tools for decision‑makers.

Equal Employment Opportunity

Block is an equal opportunity employer evaluating all employees and job applicants without regard to identity or any legally protected class. We will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and "fair chance" ordinances. We believe in being fair, and are committed to an inclusive interview experience, including providing reasonable accommodations to disabled applicants throughout the recruitment process. We encourage applicants to share any needed accommodations with their recruiter, who will treat these requests confidentially.

Use of AI in Our Hiring Process

We may use automated AI tools to evaluate job applications for efficiency and consistency. These tools comply with local regulations, including bias audits, and we handle all personal data in accordance with state and local privacy laws. Contact us at [e-mail] with hiring practice or data usage questions.

Salary Zones

• Zone A: $189,000—$283,600 USD
• Zone B: $179,600—$269,400 USD
• Zone C: $170,100—$255,100 USD
• Zone D: $160,700—$241,100 USD

Location

San Francisco, CA

Application

While there is no specific deadline to apply for this role, U.S. roles are typically open for an average of 55 days before being filled by a successful candidate. Please refer to the date listed at the top of this job page for when this role was first posted.