Senior Software Engineer - Platform

About the role

About us:

Calder is solving the data bottleneck for embodied AI and world models. Today's AI models lack the real-world, 4D data needed to reason on spatial problems and operate in physical environments. We capture, process, and deliver high-fidelity human-generated spatial data at scale, providing the substrate that trains the next generation of generalizable models for manipulation, navigation, and interaction.

About the Role:

You'll architect and scale the infrastructure that processes terabytes of 3D data daily, ensuring reliability, cost efficiency, and fast iteration for the engineering teams that depend on it.

- Engineer Calder's data processing infrastructure — compute orchestration, storage, scheduling, and monitoring

- Design and operate scalable GPU and CPU compute pipelines for 3D data processing workloads (SLAM, reconstruction, Gaussian Splatting training, dataset extraction)

- Assist in the development of pipeline orchestration systems, ensuring workloads run reliably, are observable, and recover gracefully from failures

- Manage and leverage cloud infrastructure (AWS) including ECS/Fargate, EC2 GPU instances, S3, and networking — balancing performance with cost

- Build and maintain data versioning and lineage infrastructure (e.g., LakeFS, data lake architecture)

- Contribute to SLAs for data processing throughput and reliability; instrument pipelines with metrics and alerting

- Utilize infrastructure-as-code practices (Terraform) and CI/CD for infrastructure changes

- Collaborate with 3DGS, SLAM, QA, and delivery teams to understand workload requirements and translate them into platform capabilities

- Mentor and grow yourself and others in a small infrastructure engineering team

About you:

- Large scale production experience in infrastructure engineering, platform engineering, or MLOps.

- Experience with AWS (ECS, EC2, S3, IAM, VPC, CloudWatch) or equivalent cloud platform

- Strong grasp of container orchestration (Docker, ECS, Kubernetes) and compute scheduling

- Experience managing GPU workloads at scale (training, inference, or batch processing)

- Proficiency with infrastructure-as-code (Terraform, Pulumi, or CloudFormation)

- Hands-on experience with pipeline orchestration frameworks (Dagster, Airflow, Prefect, or similar)

- Strong systems thinking — you understand how to design for reliability, observability, and cost at scale

- B.S. or M.S. in Computer Science, Software Engineering, or a related field

Even better:

- Experience with data versioning systems (LakeFS, DVC, Pachyderm)

- Background in 3D data processing, computer vision, or robotics infrastructure

- Familiarity with medallion / data lake architectures (bronze → silver → gold)

- Experience with Datadog, OpenTelemetry, or similar observability stacks