Chime Financial, Inc
financial technology
SoftwareEngineer,MachineLearningPlatform
“Software Engineer, Machine Learning Platform at Chime Financial, Inc. Skills: ML infrastructure, platform engineering, distributed systems, cloud computing, applied machine learning, AWS, Ray, Terraform, Docker, Kubernetes, Python, Go, Scala, Java. Design, build, and operate scalable ML infrastructure on AWS. Develop distributed training and batch processing systems using Ray”
What You'll Achieve.
enable data scientists and ML engineers to develop, train, deploy, and monitor models reliably and efficiently; building robust foundations that allow ML teams to move quickly while maintaining reliability, governance, and cost efficiency; help millions unlock their financial potential
Industry & Context.
problem solvers
Participate in on-call rotations to support production systems
What They're Looking For.
Must Have
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, 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, 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)
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
What You'll Do.
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.
or similar technologies)
Improve CI/CD workflows for ML models and platform components
Enhance observability
and cost visibility across ML workloads
Contribute to platform architecture decisions and technical roadmaps
Participate in on-call rotations to support production systems
How You'll Work.
Team & Collaboration
Partner closely with Data Science and ML Engineering teams to improve developer experience; Contribute to platform architecture decisions and technical roadmaps; working together; embracing diverse perspectives; giving each other honest feedback
Applying for this Software Engineer, Machine Learning Platform role?
Most applicants get filtered before a human reads their resume. See if yours makes the cut.
How to Apply on Greenhouse
- Create a Greenhouse profile before applying — it saves time across multiple applications.
- Upload your resume as a PDF; the parser handles it better than Word.
- Answer all knockout questions carefully — wrong answers auto-reject before a human sees you.
- Enable email notifications to track application status in real time.
ANONYMOUS · UNFILTERED
What do employees actually say about Chime Financial, Inc?
Real rants from real employees. Read before you apply.