Software Engineer, Machine Learning Platform
About The Position
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.
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)
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, etc.)
- Experience supporting ML lifecycle workflows (training, evaluation, deployment, monitoring)
- Knowledge of ML experimentation platforms and model governance practices
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
Benefits
- Bonus
- Competitive equity package
- 401k match
- Medical benefits
- Dental benefits
- Vision benefits
- Life benefits
- Disability benefits
- Generous vacation policy
- Company-wide Chime Days
- Company-wide paid days off
- 1% of your time off to support local community organizations
- Annual wellness stipend
- Up to 24 weeks of paid parental leave for birthing parents
- 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-office perks including backup child, elder, and/or pet care
- Subsidized commuter benefit
- In-person and virtual events to connect with your fellow Chimers
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
