Job description

At Rhoda AI, we’re building the next generation of generalist intelligent robots. We own the full robotics stack from high-performance hardware and robot systems to the infrastructure and state-of-the-art foundation world models that control our robots. Our robots are designed to be generalists capable of operating in complex, real-world environments and handling long-tail edge cases, made possible by our cutting edge research and end-to-end system design. We've raised over $450M and are investing aggressively in model research, infrastructure, hardware development, and manufacturing scale-up to make generalist robotics a reality. We're looking for a Cloud Infrastructure Engineer to build and operate the systems that power our robotics and AI platform. You'll own the infrastructure that collects training data, keeps our robots running in the field, and trains and evaluates our models — designing for high reliability and low latency across every layer. The systems you build will be the backbone of what we ship. 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

Requirements

Must-haves

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

Nice-to-haves

Nice to Have (But Not Required)

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiawsgcpazurepythongokubernetesml

Platform: ashby
Effort: quick