Tasks:
- * Build self-service platform portal, CLI, APIs, and SDKs
- * Design embodied AI training and inference platform
- * Improve platform reliability, observability, and resource efficiency
- * Manage artifact versioning, lineage, reproducibility, and auditability
- * Orchestrate end-to-end workflows and resource scheduling
Perks/Benefits:
Skills/Tech stack required:
[Artifact management] [C++] [DAG scheduling] [Database systems] [Data Versioning] [Distributed Systems] [Docker] [Go] [Inference platforms] [Java] [Kubernetes] [Message Queues] [Microservices] [MLOps] [Model Evaluation] [Model lineage] [Model Management] [Model Training] [Model training platforms] [Object storage] [Observability] [Python] [Resource scheduling] [Training platforms] [Workflow Orchestration]
Educational requirements:
[Bachelor's Degree]
Role(s):
[AI Platform Engineer] [Backend Engineer] [Distributed Systems Engineer] [Engineer] [MLOps Platform Engineer] [Platform Engineer] [Systems Engineer]