Member of Technical Staff; Backend & Infrastructure

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. We are 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.

• Core
  
  Languages:
  
  Python is our go-to for backend development, data pipelines, and ML integration. Deep experience with modern Python is a must.

• Databases & Storage: We operate a large Click House cluster for analytics workloads and Postgre
  
  SQL for transactional data. Experience with distributed databases and data modeling is highly valued.

• Data Pipelines & Orchestration: We build reliable, scalable data pipelines using Dagster for orchestration and comprehensive observability.

• Cloud & Infrastructure: We use a mix of cloud (AWS) and bare metal infrastructure, with tools like Terraform for infrastructure-as-code.

• 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 database systems 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, refine system architecture, 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.

• 3+ years of experience in a Software Engineering role.

• Strong experience in deploying and supporting large-scale production systems.

• Ability to make pragmatic, fast-paced architectural 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.

• Gained deep product immersion:
  Understood the “what” and “why” of the product, contributed to discussions, formed opinions and comfortably expressed them, and actively participated in team retros.

• Deployed scoped components:
  Delivered isolated, limited-scope features, addressed feedback, and deployed quickly with minimal supervision, making confident…