Cloud Infrastructure Engineer

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

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

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

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