Top Remote Platform Engineer Jobs

Build and operate next-generation ML data and feature infrastructure, including near-real-time feature computation engines, feature stores, and embedding retrieval systems. Scale data processing, storage, and serving for high-throughput training and low-latency inference. Collaborate with ML practitioners and AI Platform teams to improve feature discovery, sharing, and practitioner productivity.

Design, build, and operate cloud-native, large-scale distributed data infrastructure (Spark, Presto/Trino, Druid, Iceberg). Improve performance, scalability, and reliability, collaborate cross-functionally, and contribute to open-source projects.

Design and build scalable training platform infrastructure for large distributed ML model training. Improve performance, reliability, and cost-effectiveness of training jobs. Build APIs/UX for ML engineers, diagnose GPU/communication/memory bottlenecks, and implement observability, fault tolerance, and on-call practices while partnering with ML researchers and infrastructure teams.

Design, build, deploy, and operate scalable cloud-native microservices and APIs (Java/Spring Boot) on AWS/GCP and Kubernetes. Build event-driven systems with Kafka, ensure reliability via TDD, CI/CD, monitoring, and performance testing, and collaborate with cross-functional teams to deliver production services at global scale.

Build and scale AI data and ML infrastructure: evaluation systems, fine-tuning pipelines, agent-first product surfaces, high-throughput data workflows, and integrations of new models into production.

Lead cloud-native platform engineering initiatives: migrate Jenkins pipelines to Harness, move GCVE/on‑prem workloads to GKE, implement GitOps, IaC (Terraform/Ansible), enforce cloud governance and security, build observability and scalable GKE infrastructure, and coach teams on agile and best practices to improve developer productivity.