Senior/Staff Cloud Infrastructure Engineer

What You'll Do

• Design, build, and maintain cloud infrastructure supporting data collection pipelines, robot operations, and model training and evaluation workflows
• Own the reliability, availability, and latency of core infrastructure including databases, data warehouses, and object storage systems
• Develop and maintain backend services and APIs that expose infrastructure capabilities to internal teams and customers
• Identify and resolve performance bottlenecks across the data and compute stack to meet latency and throughput requirements
• Partner with research teams to understand model training and evaluation infrastructure needs and translate them into scalable solutions
• Collaborate with robotics teams to ensure field operations are reliably supported by low-latency backend services
• Build observability tooling - metrics, logging, alerting - to proactively detect and respond to infrastructure issues
• Define and enforce infrastructure best practices around security, cost management, and scalability
• Participate in on-call rotations and contribute to incident response and postmortems

What We're Looking For

• 4+ years of experience in cloud infrastructure, platform engineering, or a related role
• Strong proficiency with at least one major cloud provider (AWS, GCP, or Azure), including compute, networking, storage, and managed database services
• Hands-on experience managing relational and NoSQL databases in production, including performance tuning, replication, and failover
• Experience operating data warehouse solutions (e.g., BigQuery, Redshift, Snowflake) and large-scale object storage (e.g., S3, GCS)
• Solid backend development skills - comfortable writing and maintaining services in Python, Go, or a similar language
• Strong understanding of distributed systems concepts: consistency, availability, fault tolerance, and latency trade-offs
• Familiarity with container orchestration using Kubernetes or equivalent platforms
• Proven ability to debug and resolve complex production incidents under pressure

Nice to Have (But Not Required)

• Experience building infrastructure for ML workloads - GPU cluster management, distributed training frameworks, or model serving pipelines
• Familiarity with robotics or embedded systems backends, including real-time telemetry or command-and-control infrastructure
• Experience designing and operating high-throughput, low-latency data pipelines using tools like Kafka, Flink, or Spark
• Background working with time-series databases (e.g., InfluxDB, TimescaleDB) for sensor or operational data
• Experience with infrastructure-as-code tools such as Terraform or Pulumi
• Track record of building multi-tenant infrastructure that serves diverse customer and internal stakeholder needs simultaneously
• Experience building self-service infrastructure platforms that reduce engineering toil for research or product teams

Why This Role

• Own the infrastructure layer that everything else runs on - from robot field ops to model training - with direct, measurable impact on reliability and research velocity
• Work at the intersection of cloud systems and physical AI, building backends that support both frontier model training and real humanoids operating in the world
• Foundational role on a small team where your architectural decisions shape the platform the entire company scales on