Senior Software Engineer, Machine Learning Platform

Chime; San Francisco (CA); On-site; USD 187,000 - 259,000; Full time

Benefits offered by this job

Bonus opportunities

Equity package

Benefits package

Job summary

Chime is looking for a Machine Learning Engineer to design and operate scalable Machine Learning infrastructure on AWS. The ideal candidate should have over 5 years of experience in ML infrastructure and platform engineering, with a strong programming background in Python or similar languages.

The role offers a base salary between $187,000 and $259,000, along with eligibility for bonuses and equity packages. Join Chime's innovative MLP team to further enhance data science capabilities.

Qualifications

• 5+ years of experience in ML infrastructure or platform engineering.
• Ability to design scalable ML infrastructure on AWS.
• Experience with containerization and orchestration.

Responsibilities

• Develop and operate scalable ML infrastructure on AWS.
• Build data ingestion and streaming systems.
• Enhance observability and reliability of ML workloads.

Skills

ML infrastructure experience

Distributed systems knowledge

Cloud computing expertise

Programming skills in Python

Containerization technologies (Docker, Kubernetes)

Education

Bachelor's Degree in Computer Science or related field

Tools

AWS

Terraform

Ray

Spark

CI/CD pipelines

Job description

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. The base salary offered for this role and level of experience will begin at $187,000.00 and go up to $259,000.00. Full‑time employees are also eligible for a bonus, competitive equity package, and benefits.

In this role, you can expect to

• 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 such as Kinesis, Kafka, Flink, and Spark
• 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

To thrive in this role

• 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 cost optimization
• Strong programming skills in Python, Go, Scala, Java or similar languages
• Familiarity with infrastructure‑as‑code such as Terraform and CloudFormation
• Solid understanding of software engineering fundamentals (testing, version control, code review, observability)

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 such as Kafka, Kinesis, Flink, Spark Streaming
• Experience supporting ML lifecycle workflows (training, evaluation, deployment, monitoring)
• Knowledge of ML experimentation platforms and model governance practices

Chime is proud to be an 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. If you have a disability or special need that requires accommodation during any stage of the application process, please contact: [e-mail].

To learn more about how Chime collects and uses your personal information during the application process, please see the Chime Applicant Privacy Notice.