AI/ML Engineer

Socket.dev; San Francisco (CA); On-site; USD 110,000 - 150,000; Full time

Job summary

Chime is seeking an AI/ML Engineer to join their Trust & Safety team, working in San Francisco. This role involves building models and decision systems aimed at protecting millions of members while fostering safe financial progress.

The ideal candidate will contribute to model development, collaborate with various teams, and improve risk detection and member experience. A strong background in machine learning and data science, along with effective communication skills, is essential.

Qualifications

• 1–2 years of experience in applied data science or AI/ML engineering.
• Familiarity with cloud platforms and orchestration tools.
• Ability to communicate analyses and collaborate effectively.

Responsibilities

• Contribute to end-to-end model development for trust and safety.
• Collaborate with Product Managers to translate findings into recommendations.
• Support offline model evaluation and contribute to online analysis.

Skills

Machine learning fundamentals

Data analysis skills

Communication skills

Cloud platforms (AWS)

Deep learning methods

Education

1–2 years of experience in data science or AI/ML

Tools

AWS

Version control

AI-assisted development tools

Job description

Requirements

• We are looking for an AI/ML Engineer who is growing strong technical depth in machine learning, experimentation, and analytical problem solving, with an interest in applying those skills to trust, safety, and risk challenges across Chime
• 1–2 years of experience in applied data science or AI/ML engineering, including relevant internship, academic, or project experience
• Working knowledge of machine learning fundamentals, including feature development, model training, validation, tuning, and evaluation
• Familiarity with cloud platforms, preferably AWS, orchestration tools, and version control
• Familiarity with offline model evaluation, experimentation, and model performance tradeoffs
• Ability to communicate analyses and model findings clearly, clarify requirements, and collaborate effectively with technical and non-technical partners
• (Desirable) Exposure to production ML workflows, including inference, monitoring, retraining, orchestration, model deployment
• (Desirable) Experience using AI‑assisted development tools such as Cursor, Claude Code, or similar tools
• (Desirable) Exposure to deep learning methods, such as embeddings, sequence models, representation learning, or behavioral modeling

What the job involves

• Chime’s AI/ML Trust & Safety team is building models, insights, and decisioning systems that help protect millions of members while enabling safe, reliable financial progress
• You’ll contribute to end-to-end model development, analysis, and production‑facing decision systems with guidance from senior team members. Your work will help improve how Chime detects risk, understands member behavior, evaluates tradeoffs, and scales trustworthy member experiences
• This role is a strong opportunity to build deeper expertise in applied ML, risk systems, experimentation, generative AI, sequence models, and cross‑functional product impact in a high‑scale environment
• Contribute to the design and implementation of training pipeline components for AI/ML models that support Chime’s risk decisioning systems
• Develop, test, and iterate on model features within clear requirements and with support from senior team members
• Support offline model evaluation and contribute to online experiment analysis to understand performance, tradeoffs, and member impact
• Write modular, testable, and maintainable code following engineering best practices
• Collaborate with Product Managers, Engineers, and Risk teams to translate model findings into clear recommendations and measurable member impact
• Contribute to production‑facing model workflows, including model training, tuning, inference, and monitoring
• Contribute to projects that apply modern AI/ML methods, such as generative AI, sequence models, and automation workflows, to improve Risk decisioning