Chime Financial, Inc - Senior Software Engineer, Machine Learning Platform
Requirements
• ✨ 401k match plus great medical, dental, vision, life, and disability benefits • 🏝 Generous vacation policy and company-wide Chime Days, bonus company-wide paid days off • 🫂 1% of your time off to support local community organizations of your choice • 👟 Annual wellness stipend to use towards eligible wellness related expenses • 👶 Up to 24 weeks of paid parental leave for birthing parents and 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-person and virtual events to connect with your fellow Chimers—think cooking classes, guided meditations, music festivals, mixology classes, paint nights, etc., and delicious snack boxes, too! • 💚 A challenging and fulfilling opportunity to join one of the most experienced teams in FinTech and help millions unlock financial progress • We know that great work can’t be done without a diverse team and inclusive environment. That’s why we specifically look for individuals of varying strengths, skills, backgrounds, and ideas to join our team. We believe this gives us a competitive advantage to better serve our members and helps us all grow as Chimers and individuals. • Autofill with MyGreenhouse
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 • 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
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