AI/ML Engineer

Chime

Hybrid

Quick summary

Work type: Hybrid
Location: San Francisco, CA
Salary: $173,000–$173,000 / yr
Posted: 6 days ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $198k

This role $173k

$147k most similar roles pay here $244k

This role pays less than 68% of similar roles. Most pay $162,000–$235,000 — the shaded band above. At the midpoint, this role pays about $173k versus about $198k for comparable roles.

Based on 240 similar postings.

Employer

About Chime

Chime is a financial technology company offering mobile-first banking services including fee-free checking accounts, savings accounts, and a secured credit builder card through partner banks. Industry: Financial Technology & Neobanking

Chime currently has 26 open roles on FindRole.

Listed pay typically runs $207,000–$207,000 across 26 roles with salary data.

Most-posted roles

View all roles at Chime

At a glance

TL;DR · AI/ML Engineer

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As part of Chime’s AI/ML Trust & Safety team, you’ll join a dynamic group focused on developing and deploying machine learning models to enhance member safety and risk management. As an AI/ML Engineer at this level, your responsibilities include designing training pipelines for decisioning systems, developing model features with guidance from senior members, and conducting offline evaluations and online experiments to assess performance and impact. You will also collaborate closely with cross-functional teams to translate analytical insights into actionable recommendations that drive measurable improvements in risk management. This role requires proficiency in machine learning fundamentals, cloud platforms like AWS, version control, and experience with modern AI/ML methods such as generative models and sequence analysis. Ideal candidates have a background in applied data science or engineering and are eager to contribute to high-scale systems addressing complex trust and safety challenges.

Skills

Python AWS Kubernetes Terraform Docker CI/CD Prometheus Grafana PostgreSQL TensorFlow PyTorch Scikit-learn Pandas Jupyter Git MLflow Airflow Snowflake Redis

What you'll do

Design and implement training pipeline components for AI/ML models supporting risk decisioning.
Develop, test, and iterate on model features based on clear requirements with senior guidance.
Support offline model evaluation and online experiment analysis to assess performance and impact.
Write modular, maintainable code following engineering best practices for production systems.
Contribute to projects applying modern AI/ML methods like generative AI and sequence models.
Collaborate on production-facing workflows including model training, tuning, inference, and monitoring.

What we're looking for

1-2 years of experience in applied data science or AI/ML engineering.
Strong knowledge of machine learning fundamentals and model development processes.
Experience with cloud platforms, orchestration tools, and version control systems.
Proficiency in offline model evaluation, experimentation, and performance analysis.
Ability to communicate technical findings clearly to both technical and non-technical stakeholders.
Familiarity with modern AI/ML methods like generative AI and sequence models.
Contribution to production ML workflows including inference, monitoring, and deployment.

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