About the company
Chime is a financial technology company that empowers members to achieve financial progress. The Machine Learning Platform (MLP) team builds and operates the infrastructure, tooling, and developer experience that powers machine learning across the company.
Responsibilities
- Design, build, and operate scalable ML and AI infrastructure on AWS
- Design and operate shared platform capabilities for LLM and agentic workloads, including model access, prompt and configuration lifecycle, retrieval, tool integration, state management, and workflow orchestration
- Build evaluation frameworks for non-deterministic AI systems, including offline benchmarks, regression testing, online quality signals, human feedback, and failure analysis
- Establish observability, reliability, and governance for models and agents
- Help teams make principled architecture decisions across traditional ML, LLM-powered applications, and agentic workflows
- Build distributed training, batch inference, and large-scale processing systems using frameworks such as Ray or Spark
- Build and maintain infrastructure as code using Terraform
- Support and evolve the feature store and feature pipelines
- Develop data ingestion and streaming systems using technologies such as Kinesis, Kafka, Flink, or Spark
- Improve CI/CD workflows for ML models, AI applications, and platform components
- Partner closely with Data Science and ML Engineering teams
- Participate in on-call rotations
Requirements
- Knowledge of the machine learning development lifecycle, including data preprocessing, model training, evaluation, deployment, and monitoring
- Experience designing distributed systems and large-scale data or compute platforms on AWS using frameworks such as Spark or Ray
- 5+ years of experience in ML or AI infrastructure, platform engineering, distributed systems, or production ML systems
- Working knowledge of LLM application patterns such as retrieval-augmented generation, structured outputs, tool calling, agent orchestration, and evaluation of non-deterministic systems
- Experience designing production systems that integrate ML or foundation models through reliable APIs, workflows, and data contracts
- Hands-on experience with CI/CD pipelines, DevOps practices, and infrastructure as code
- Experience with containerization and orchestration technologies such as Docker and Kubernetes
- Strong programming skills in Python, Go, Scala, Java, or similar languages
- Solid understanding of software engineering fundamentals
Nice-to-have
- Experience shipping LLM-powered or agentic systems to production
- Experience with model gateways, prompt lifecycle management, retrieval or vector search, tool execution, and agent orchestration frameworks
- Experience building evaluation, tracing, and observability capabilities for non-deterministic AI systems
- Familiarity with managed or self-hosted foundation model infrastructure, such as Amazon Bedrock, SageMaker, or equivalent platforms
- Experience operating GPU-based workloads and optimizing training or inference performance and cost; CUDA experience is a plus
Conditions
- Base salary: $187,000 - $259,000 per year
- Full-time employees are eligible for a bonus, competitive equity package, and benefits