Staff Backend Engineer

About Opaque Systems, Inc.

WHAT WE DO

Opaque is the confidential AI platform unlocking sensitive data to securely accelerate AI into production. Created by world-renowned researchers at the Berkeley RISELab, Opaque’s user-friendly platform empowers organizations to run cloud-scale, general purpose AI workloads on encrypted data. Opaque supports popular languages and frameworks for AI, including Python and Spark, and enables governed data sharing with cryptographic verification of privacy and sovereignty. Opaque customers deploy high-performance AI faster and eliminate the tradeoff between innovation and security.

WHO WE ARE

At Opaque, we cultivate an effective work culture grounded in kindness, customer-centricity, and continuous improvement. By fostering innovation, inclusivity, and excellence, we attract top talent and set industry standards, leading to widespread adoption and trust in AI technologies that keep data private and sovereign.

ABOUT THIS JOB

As a Staff Backend Engineer at Opaque Systems, you will lead the development of secure, scalable systems that power data analytics, machine learning pipelines, and AI agents. You will architect backend infrastructure to support complex workloads, guide long-term technical decisions in ambiguous areas, and collaborate across teams to build robust, privacy-aware solutions.

Key Responsibilities

• Design and build backend services that enable secure, high-throughput data analytics and machine learning workflows.
• Architect systems to support the orchestration, execution, and monitoring of ML pipelines and AI agent workflows at scale.
• Develop APIs and internal interfaces that allow seamless integration between data pipelines, ML models, and external platforms.
• Make architectural decisions that prioritize extensibility and performance across heterogeneous compute and data environments.
• Integrate secure computation, access control, and auditability into core infrastructure to support compliance and data protection.
• Mentor engineers across domains and guide system-wide improvements in observability, reliability, and code quality.
• Lead technical discovery in ambiguous problem spaces and define patterns for scalable data + ML system design.
• Stay current with trends in AI infrastructure, agent frameworks, and secure data analytics to inform architectural strategy.

Qualifications