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Member of Technical Staff – Product Engineering

causal · San Francisco

🇬🇧 English
Backend systems GCP AWS Azure Modern frontend frameworks Docker Kubernetes Terraform Model serving

Job description

About the role

Join causal’s Product Engineering team to bridge cutting‑edge research and real‑world impact. You will own the end‑to‑end path from trained large physics models to reliable, customer‑facing products that predict and influence physical systems.

Key responsibilities

  • Build and operate production systems that deliver model predictions under hard real‑time deadlines, ensuring reliability, cost efficiency, monitoring, and incident response.
  • Design and implement the full product surface, including backend APIs, data delivery pipelines, integration patterns, and frontend dashboards/visualizations.
  • Package, secure, observe, and upgrade deployments for diverse customer environments (cloud, VPC, on‑prem, restricted networks).
  • Create demos and prototypes for prospective customers, iterating rapidly with go‑to‑market teams.
  • Work directly in customer environments to integrate with their data and systems, then translate insights into product requirements.
  • Develop tooling and playbooks that enable solutions built for one customer to generalize to others.

Required profile

  • Relentless problem‑solver with rapid execution and quick learning in unfamiliar domains.
  • Strong generalist software engineering background across backend, APIs, cloud infrastructure, and modern frontend frameworks.
  • Experience deploying and operating machine‑learning systems in production, especially in customer‑controlled environments.
  • Comfortable engaging directly with customers to scope ambiguous problems, build time‑pressured demos, and represent the company technically.

Required skills

  • Backend systems and API development
  • Cloud platforms (GCP, AWS, Azure)
  • Modern frontend frameworks
  • Containerization and orchestration (Docker, Kubernetes)
  • Infrastructure‑as‑code tools (Terraform)
  • Scalable model serving and deployment architectures

Questions fréquentes

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Source : ats:ashby

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Published 1 month ago

Expires 1 week from now

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causal

San Francisco