Tech Stack
Distributed SystemsSQLTorchserveVllmTritonRetrieval SystemsText Embedding ModelsImage Embedding ModelsNode.jsTypeScriptRustPostgreSQLVector DatabasesKubernetesGCPAWSAzureMachine LearningModel ServingREST ApisGoS3BigQueryRedshiftObservabilityPerformance MonitoringReal-Time AnalyticsMultimodal Sensor ProcessingData MiningInfrastructure As CodeTerraformCloud Security
Solid badges = required, outlined = preferred
Responsibilities
- Design and build search and curation systems for enabling ML engineer customers to inspect and utilize data.
- Build low-latency, random-access playback systems that let users explore live or recorded data across devices, time ranges, and sensor modalities.
- Stand up semantic and hybrid search over multimodal robotics data, including vector database integrations and embedding application pipelines.
- Deploy and operate inference infrastructure for production ML workloads: model serving, scaling, latency and cost optimization.
- Architect and maintain multi-cloud deployments across GCP, AWS, and Azure, including components that run inside customer-managed environments.
Soft Skills
Written CommunicationVerbal CommunicationTechnical Decision-MakingCustomer FocusRoboticsAutonomous VehiclesPhysical AI
Culture
Fast-PacedStartup EnergyHigh AutonomyMission-DrivenOpen Source Contributors
Requirements
Regions: Us
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About Foxglove
Industry: robotics
Size: startup
Foxglove builds observability, visualization, and data infrastructure tools for robotics and autonomous systems teams to manage and analyze massive volumes of multimodal sensor data. They enable engineers to ingest, store, query, replay, and analyze data from live systems and production fleets.
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