Menlo
Platform Engineer
Remote Platform Engineer role with clear candidate location fit.
PostedJun 18, 2026
Eligible countries1 accepted country
Seniority signalMiddle
Work settingRemote
Accepted candidate locations
Vietnam
Role overview
Platform Engineer
Requirements and responsibilities
Readable role content extracted into sections for faster review.
What You'll Do
- Build and maintain distributed infrastructure handling telemetry, sensory, and control data across cloud and edge environments
- Design and operate data ingestion and streaming pipelines connecting robot fleets to the cloud in real time, covering video, joint states, audio, and LiDAR
- Develop and maintain backend services and APIs that power Menlo's developer-facing platform, with a focus on reliability and developer experience
- Manage and evolve cloud native infrastructure using Kubernetes, Docker, and infrastructure as code tooling
- Ensure platform reliability through monitoring, alerting, autoscaling, failover, and incident response
- Support ML and robotics teams with data infrastructure for training pipelines, policy rollout, and hardware-in-the-loop simulation
- Implement secure APIs with access control, rate limiting, and usage metering as we scale
What You'll Bring
- 4 or more years of professional software engineering experience in platform, infrastructure, or data engineering
- Proficiency in one or more of Go, Rust, Python, or TypeScript, with strong fundamentals in concurrency and systems performance
- Hands-on experience with cloud native tooling: Kubernetes, Docker, Helm, and gRPC
- Experience building and operating data pipelines and streaming systems -- Kafka, Flink, or similar
- Solid understanding of API design patterns including REST, gRPC, and WebSockets
- Experience with databases spanning PostgreSQL, Redis, and modern vector databases
- Familiarity with observability tooling: Prometheus, Grafana, Datadog, or OpenTelemetry
Bonus Points
- Experience with real-time data streams from physical sensors or robotics systems
- Familiarity with MLOps workflows including model versioning, inference pipelines, and model registries
- Background in distributed training or large-scale simulation infrastructure
- Contributions to open-source infrastructure, robotics middleware, or AI frameworks
- Experience on developer platforms or API products
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