Systems Engineering Intern
At Vesta, we are revolutionizing the way businesses and consumers approach fraud prevention and payment security. Our mission is to create a world where trust is inherent in every transaction. Leveraging cutting-edge machine learning, data analytics, and real-time decisioning, we empower businesses to eliminate fraud and unlock new growth opportunities.
As a Systems Engineering Intern on our Infrastructure & Engineering Team, you will gain hands-on experience in benchmarking, analyzing, and optimizing the performance of distributed in-memory data stores. You will work alongside a team of experienced engineers to evaluate real-world deployments of technologies such as Redis, Valkey, and KeyDB, driving insights that enhance system reliability and efficiency. This internship offers exposure to modern DevOps practices, Kubernetes orchestration, and microservice architectures, with an opportunity to develop hands-on experience in performance tuning and system profiling.
Key Responsibilities
- Assist in designing and running performance tests and benchmarks for Redis, Valkey, and KeyDB under various load conditions.
- Support the creation of performance baselines across different deployment models (bare metal, containers, cloud-native).
- Analyze results and contribute to documentation of performance findings and tuning recommendations.
- Help manage containerized services using Kubernetes in a microservices environment.
- Write and maintain Python scripts to automate test execution, result parsing, and system monitoring.
- Contribute to environment setup and tuning on Linux-based systems used in benchmarking workflows.
Preferred Qualifications
- Currently pursuing a bachelor’s degree in Computer Science, Information Systems, or a related technical field.
- Familiarity with Redis, Valkey, or KeyDB, or a strong desire to learn in-memory data technologies.
- Basic experience with Kubernetes and an understanding of container orchestration principles.
- Exposure to microservice-based application deployment models.
- Proficiency or coursework in Python scripting.
- Comfortable working in Linux environments, including command-line usage and basic system administration.