# Software Engineer, Machine Learning Platform

**Company:** [Chime](https://hotfix.jobs/companies/chime)
**Location:** San Francisco, CA
**Role:** ML Engineering
**Salary:** $187k – $259k/yr
**Experience:** 5+ years
**Skills:** Python, Go, Scala, Java, AWS, Kubernetes, Docker, Terraform, Ray, Spark, Kafka, Flink, CUDA
**Posted:** 2026-05-15

> Build and operate Chime's ML platform on AWS, including distributed training systems, feature stores, data pipelines, and CI/CD tooling. Partner with ML teams to improve reliability, observability, and developer experience for production models.

## Job Description

## 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)
- 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 (data preprocessing, model training, evaluation, deployment)
- Experience with distributed systems, cloud computing, or large-scale data processing
- Hands-on experience with CI/CD pipelines, DevOps practices, and infrastructure as code
- Experience with containerization technologies (Docker, Kubernetes)
- Knowledge of cloud platforms (AWS) and distributed computing frameworks (Spark, Ray)
- Experience with GPU programming (CUDA) and GPU optimization
- Strong programming skills in Python, Go, Scala, Java or similar languages
- Familiarity with infrastructure-as-code (Terraform, CloudFormation)
- Solid understanding of software engineering fundamentals (testing, version control, code review, observability)

## Nice-to-Haves
- 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)
- Experience supporting ML lifecycle workflows
- Knowledge of ML experimentation platforms and model governance practices

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**Apply:** https://hotfix.jobs/jobs/8227a48c-d3ba-4a85-afb5-a934accf63b3
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