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.
As a Machine Learning Platform Engineer, you will design and build scalable systems that support model training, feature computation, real-time inference, and experimentation. You’ll work at the intersection of distributed systems, cloud infrastructure, and applied machine learning. This role focuses on building robust foundations that allow ML teams to move quickly while maintaining reliability, governance, and cost efficiency.
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 (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
To thrive in this role, you 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
- 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)
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