Software Engineer, Machine Learning Platform

About the role

Chime’s Machine Learning Platform (MLP) team builds and operates the infrastructure, tooling, and developer experience that powers machine learning across the company. We enable data scientists and ML engineers to develop, train, deploy, and monitor models reliably and efficiently.

Responsibilities

• Design, build, and operate scalable ML infrastructure on AWS
• Develop distributed training and batch processing systems using Ray
• Build and maintain infrastructure-as-code using Terraform
• Support and evolve the feature store and feature pipelines
• Develop data ingestion and streaming systems (e.g., Kinesis, Kafka, Flink, Spark, or similar technologies)
• Improve CI/CD workflows for ML models and platform components
• Enhance observability, reliability, and cost visibility across ML workloads
• Partner closely with Data Science and ML Engineering teams to improve developer experience
• Contribute to platform architecture decisions and technical roadmaps
• Participate in on-call rotations to support production systems

Requirements

• 5+ years of experience in ML infrastructure, platform engineering, or production ML systems
• Knowledge of the machine learning model development lifecycle, including data preprocessing, model training, evaluation, and deployment
• Experience with distributed systems, cloud computing, or large-scale data processing
• Strong foundation in computer science and software engineering principles
• Deeply interested in the impact and evolution of advanced AI technologies
• Hands-on experience with CI/CD pipelines, DevOps practices, and infrastructure as code
• Experience with containerization technologies such as Docker and Kubernetes, and orchestration systems
• Knowledge of cloud platforms such as AWS and distributed computing frameworks such as Spark and Ray
• Experience with GPU programming(CUDA) and GPU costs/optimization
• Strong programming skills in Python, Go, Scala, Java or similar languages
• Familiarity with infrastructure-as-code (e.g., Terraform, CloudFormation)
• Solid understanding of software engineering fundamentals (testing, version control, code review, observability)

Qualifications

• Nice-to-have: Experience with distributed compute frameworks such as Ray, experience building or operating a feature store, experience with real-time ML systems or model serving, familiarity with streaming technologies (Kafka, Kinesis, Flink, Spark Streaming, etc.), experience supporting ML lifecycle workflows (training, evaluation, deployment, monitoring), knowledge of ML experimentation platforms and model governance practices

Skills

• Experience with distributed systems, cloud computing, or large-scale data processing
• Strong foundation in computer science and software engineering principles
• Hands-on experience with CI/CD pipelines, DevOps practices, and infrastructure as code
• Experience with containerization technologies such as Docker and Kubernetes, and orchestration systems
• Knowledge of cloud platforms such as AWS and distributed computing frameworks such as Spark and Ray
• Experience with GPU programming(CUDA) and GPU costs/optimization
• Strong programming skills in Python, Go, Scala, Java or similar languages
• Familiarity with infrastructure-as-code (e.g., Terraform, CloudFormation)
• Solid understanding of software engineering fundamentals (testing, version control, code review, observability)

Benefits

The base salary offered for this role and level of experience will begin at $187,000.00 and goes up to $259,000.00. Full-time employees are also eligible for a bonus, competitive equity package, and benefits. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience.

Pay

The base salary offered for this role and level of experience will begin at $187,000.00 and goes up to $259,000.00. Full-time employees are also eligible for a bonus, competitive equity package, and benefits. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience.

Schedule

In this role, you can expect to work a standard full-time schedule.

Company Information

We are a financial technology company, not a bank. Banking services provided by The Bancorp Bank, N.A. or Stride Bank, N.A., Members FDIC.

What we offer for our full-time, regular employees

• In-office work policy is designed to keep you connected - with four days a week in the office and Fridays from home for those near one of our offices, plus team and company-wide events depending on location.
• In-office perks including backup child, elder, and/or pet care, plus a subsidized commuter benefit to support your regular commute.
• Competitive salary based on experience.
• 401k match plus great medical, dental, vision, life, and disability benefits.
• Generous vacation policy and company-wide Chime Days, bonus company-wide paid days off.
• Annual wellness stipend to use towards eligible wellness related expenses.
• Up to 24 weeks of paid parental leave for birthing parents and 12 weeks of paid parental leave for non-birthing parents.
• Access to Maven, a family planning tool, with $15k lifetime reimbursement for egg freezing, fertility treatments, adoption, and more.
• In-person and virtual events to connect with your fellow Chimers—think cooking classes, guided meditations, music festivals, mixology classes, paint nights, etc., and delicious snack boxes, too!
• A challenging and fulfilling opportunity to join one of the most experienced teams in FinTech and help millions unlock financial progress.

Equal Opportunity Employer

We consider qualified applicants without regard to race, color, ancestry, religion, sex, national origin, sexual orientation, gender identity, age, marital or family status, disability, genetic information, veteran status, or any other legally protected basis under provincial, federal, state, and local laws, regulations, or ordinances. We will also consider qualified applicants with criminal histories in a manner consistent with the requirements of state and local laws, including the San Francisco Fair Chance Ordinance, Cook County Ordinance, NYC Fair Chance Act, and the LA City Fair Chance Ordinance, and consistent with Canadian provincial and federal laws.