Member of Technical Staff — Product Engineering
Causal · San Francisco
About this role
## Member of Technical Staff — Product Engineering ### About the mission We’re building **general causal intelligence**—AI that can **predict the future** and **identify actions to alter it**. To do this, we’re developing a **Large Physics foundation Model (LPM)**, since physical systems have verifiable cause-and-effect. We’re looking for software engineers excited to tackle unsolved problems and help bring frontier models into reliable, customer-ready production systems. ### What you’ll do - **Build and operate production systems** that deliver model predictions to customers with **hard real-time deadlines** - **Own reliability end-to-end**, including cost efficiency, monitoring, alerting, and incident response - **Design and build the full product surface**: backend APIs, data delivery, integration patterns, and frontend dashboards/visualizations - **Own packaging, security, observability, and upgrade machinery** to deploy into customer environments (cloud, VPC, on-prem, restricted networks) - **Create product demos and prototypes** with prospective customers and iterate quickly alongside go-to-market - **Work directly in customer environments when needed**: integrate with their data/systems, ship solutions on-site, and translate learnings into research/product requirements - **Design tooling and playbooks** so solutions generalize from one customer to the next ### What we’re looking for - A **relentless problem-solving** mindset with **rapid execution** and fast learning in unfamiliar domains - Strong **generalist software engineering** across the stack (backend systems, APIs, cloud infrastructure like **GCP/AWS/Azure**, and modern frontend frameworks) - Experience **deploying and operating ML systems in production**, ideally across diverse or customer-controlled environments - Familiarity with **containerization, orchestration, and infrastructure-as-code** (e.g., **Kubernetes, Docker, Terraform**) - Comfort working with customers: scoping ambiguous problems, building demos under time pressure, and representing the company technically - Background in **scalable model serving & deployment architectures** and the surrounding systems - Ability to **own deliverables end-to-end**, from requirements through autonomous execution
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