Arize AI
Senior AI Product Engineer, Backend
Remote (United States) · Senior
Sponsorship not specified$125k-$225kDetected 42 days ago
Backend DevelopmentAlgorithmsAWSGCPAzureKubernetesKafkaMachine LearningLLMsResearch
About the role
- Our product solutions range from clean APIs that magically instrument applications, interactive playgrounds for prompt engineering and agent development, or scaling up real-time evaluation infrastructure to handle millions of annotations per second.
Responsibilities
- Build high-volume and highly available analytics systems.
- Prototype, optimize, and maintain scalable backend services that power the Arize core platform.
- Develop and integrate collection tools for robust monitoring of ML and LLM pipelines.
- Research and implement cutting-edge visualization & dimensionality reduction algorithms in a distributed environment.
- Collaborate with our product, design, and directly with customer engineering teams to enhance and expand our product offerings.
- Contribute to the build our own in-house AI Agents
- Previous work building and operating highly complex SaaS platforms/systems.
Requirements
- 5+ years of experience working with high-performance backend systems.
- Knowledge of working with public clouds & container orchestration
- Experience with distributed stream processing
- Experience with OLAP systems.
- Knowledge of working with public clouds & container orchestration - AWS, GCP, Azure, Kubernetes, etc.
- Experience with distributed stream processing - Kafka, Gazette, or similar.
Skills
- AI is rapidly transforming the world.
- The Opportunity
Compensation
- The estimated annual salary for this role is between $125,000 - $225,000, plus a competitive equity package.
Benefits
- Design and build APIs specific to our customers' Machine Learning and LLM workflows.
Company info
- What We're Looking For
- Regularly have chats with industry experts, researchers, and ethicists across the ecosystem to advance the use of responsible AI
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This listing is sourced directly from Arize AI's careers page and normalized into a canonical job model.