Platform Engineer
Indexed description
The Role
We are looking for a Platform Engineer to build and maintain the infrastructure and data systems that power Asimov and Menlo's developer platform. You will work across cloud infrastructure, data pipelines, and production APIs -- keeping our systems reliable, scalable, and fast as our robot fleet and developer ecosystem grow. This is a hands-on engineering role for someone who is as comfortable debugging a flaky pipeline as they are designing a new ingestion architecture.
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
- 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
- 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
A Note on AI
You don't need deep AI expertise for every role, but we do expect everyone at Menlo to be intellectually curious, drawn to tinkering and discovery, and excited to use AI as a real collaborator in their work. For some roles, AI fluency is a core requirement. When that's the case, we'll say so explicitly in the qualifications. People who thrive here don't treat AI as a novelty. They use it to think better, and make their work easier for others to build on.
Equal Opportunity and Accommodations
We hire talented people from a wide range of backgrounds. If you're excited about a role but don't meet every bullet, we still encourage you to apply. Menlo Research is an equal opportunity employer and does not discriminate on the basis of any legally protected characteristic. Menlo provides reasonable accommodations during the application process. If you need one, please let your recruiter know.
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