Member technical staff; Software Engineer

Overview

Physical AI, a real model of the world, is the next trillion dollar opportunity and a foundational pillar for energy, robotics and superintelligence.

At Jua we are building it now. Our models already run on four continents and help power the energy buildout needed to support ever larger intelligence.

We are assembling a world‑class team of researchers and entrepreneurial minds to turn this model into reality at global scale. If you want the opportunity of your life, come build physical AI with us.

We’re redefining earth simulation with machine learning, building a system that outperforms traditional models in speed, precision, and accuracy. Our team moves fast, leveraging a versatile tech stack and state‑of‑the‑art cloud infrastructure to maximize performance and efficiency.

We believe in using the right tools for the job, combining robust web technologies, advanced ML frameworks, and powerful numerical computing. With a focus on type‑safe programming and infrastructure‑as‑code, we ensure scalability without excess complexity.

As a Software Engineer, you’ll help shape our backend systems, pipelines, and cloud infrastructure. You’ll thrive in ambiguity, iterate fast, and turn ideas into working prototypes. If you have a hacker’s mindset, love building from scratch, and excel in lean, high‑impact environments, we’d love to hear from you.

Location: Although we are a global team, it is a requirement that applicants are currently based in Zurich, Switzerland
, working on a Hybrid working model.

• Core
  
  Languages:
  
  Python is our go‑to for backend development, data pipelines, and ML integration. Deep experience with modern Python is a must. We also have a light dusting of Rust where performance is critical.
• Data & Geospatial Libraries: We rely heavily on libraries like Xarray, Pandas, and Polars for numerical computing and geospatial data processing. Familiarity with these tools is a big plus.
• Cloud & Infrastructure: We use a mix of multi‑cloud (GCP, AWS, Cloudflare) and bare metal infrastructure, with tools like Pulumi for infrastructure‑as‑code.
• ML Frameworks: We leverage popular ML libraries and frameworks to build and deploy models efficiently.
• Other Tools: While Python is our primary language, we believe in using the right tool for the job. You’ll have the freedom to introduce new technologies when they make sense.

• Build scalable systems: Design, develop, and deploy backend pipelines, services, and cloud infrastructure with a focus on speed, scalability, and pragmatism. You’ll work across the stack—from backend services to occasional front‑end integrations and CI/CD pipelines—in a mixed multi‑cloud and bare metal environment.
• Help to shape our architecture and roadmap: Play a key role in defining how we build and scale. As an early senior engineer, you’ll influence technical strategy, optimize system design, and ensure long‑term maintainability while moving fast.
• Bring ML models to production: Work closely with ML engineers to deploy models that power real‑world decision‑making, ensuring performance, reliability, and usability for customers, including power traders.
• Identify and tackle technical challenges: Address performance bottlenecks, refactor critical systems, and strike the right balance between rapid iteration and long‑term stability.
• Prototype and iterate quickly: Move fast, test ideas, and refine approaches with short feedback cycles to drive continuous improvement.

• Bachelor’s degree (or higher) in Computer Science or a related field.
• 5+ years of experience in a Software Engineering role.
• Strong experience with modern Python in large‑scale production systems.
• Ability to make pragmatic, fast‑paced decisions while managing technical debt effectively.
• Proactive in identifying problems, performance bottlenecks, and areas for improvement.
• Nice‑to‑have: Exposure to Data Engineering or related experience.

• Deployed code to production:
  Created a pull request, merged it, and deployed iteratively, optimizing for progress rather than perfection.
• Gained deep product immersion:
  Understood the…