Senior Production ML Systems Engineer

Causal; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Causal is seeking a software engineer to own the path from trained model to customer value. You will build production systems delivering real-time predictions, design frontend dashboards, and ensure secure deployment across cloud, VPC, on-prem, and restricted networks.

You will work directly with customers, create compelling demos, and translate learnings into requirements for research and product, while improving reliability, monitoring, and incident response.

Qualifications

• Experience building and operating production-grade software systems.
• Experience deploying ML models in production environments.
• Familiarity with cloud providers (AWS, GCP, or Azure) and container orchestration.
• Ability to work directly with customers and translate feedback into technical requirements.
• Strong ownership of end-to-end deliverables from requirements to deployment.

Responsibilities

• Build and operate production systems delivering real-time predictions.
• Design and implement the product surface including backend APIs and dashboards.
• Handle packaging, security, observability, and deployment across cloud, VPC, on-prem, and restricted networks.
• Create product demos and prototypes for prospective customers.
• Work in customer environments when needed and translate learnings into requirements.

Skills

Backend systems

APIs

Cloud infrastructure

Frontend frameworks

ML production experience

Customer-facing demos

Autonomy

Tools

Kubernetes

Docker

Terraform

CI/CD

Job description

Causal is seeking a software engineer to own the path from trained model to customer value. You will build production systems delivering real-time predictions, design frontend dashboards, and ensure secure deployment across cloud, VPC, on-prem, and restricted networks.

You will work directly with customers, create compelling demos, and translate learnings into requirements for research and product, while improving reliability, monitoring, and incident response.