Do you want to work with high-caliber professionals in a dynamic and growing company? Are you entrepreneurial, hard-working, and collegial? Join us at SB Energy, a leading infrastructure company backed by SoftBank Group and Ares Infrastructure, pairing cutting-edge innovation with best-in-class execution. Our mission is to deliver reliable and secure data center and power infrastructure to strengthen America's future.

Founded in 2019, SB Energy is a SoftBank Group company headquartered in Redwood City, California. In 2026, SoftBank Group and OpenAI announced a $1 billion investment to support SB Energy as their leading development and execution partner for data center campuses, while Ares Infrastructure continues its long-standing support of the company's growth. SB Energy develops, builds, owns, and operates some of the largest and most technically advanced energy and data center infrastructure projects in the United States. Since its founding, the company has rapidly grown into a top-tier integrated platform with more than 3 gigawatts (GW) in operation and a multi-GW pipeline of energy and data center infrastructure nationwide.

SB Energy also leverages a strong culture of innovation to identify and integrate new technologies into its projects, including its AI-based digital platform, delivering energy infrastructure that is local, reliable, and matched to load. We are building the energy and infrastructure future—today.

Come join us in accelerating the energy and infrastructure transition!

Basic Function : Build useful AI systems that people rely on in daily work. SB Energy is looking for a hands-on builder who enjoys writing code, learns unfamiliar problems quickly, and stays with a solution through deployment, debugging, and adoption.

The Manager - Forward-Deployed AI Engineering will partner with SB Energy teams, learn the workflows with domain experts, find the bottleneck, and build a practical solution. Combine deep Codex fluency with Azure engineering to ship reliable software, integrations, and personalized skills that employees actually use. Own delivery and ongoing improvement while preserving each team's context and controls. Success means better workflow quality, less manual effort, and measurable results sustained after launch.

Responsibilities

Use Codex to create reusable, personalized skills, plugins, and agents that combine instructions, context, tools, and examples for teams and users. Version them, document limits, and improve them as needs change.
Connect approved systems, documents, and data through APIs and Model Context Protocol (MCP) tools, with clear contracts, permission-aware retrieval, robust error handling, and authorized actions.
Deploy Azure solutions with sound identity, access, secrets, and networking practices. Build in monitoring and recovery, balancing reliability, delivery speed, and operating cost.
Test representative tasks and failure scenarios. Trace issues across code, data, model behavior, sites, plugins, agents, spaces, dots, and integrations; fix root causes and prevent recurring failures.
Use Codex to explore repositories, implement features, diagnose failures, and review code. Inspect generated changes, run meaningful tests, and remain accountable for correctness.
Write, ship, and maintain applications, services, data workflows, and agent integrations. Choose the simplest effective approach and carry prototypes through testing, deployment, and support.
Launch with users and make adoption part of delivery. Provide usable guides, training, and support; observe real work and resolve friction exposed by feedback.
Learn workflows, constraints, and sources of truth alongside domain experts. Turn ambiguous requests into practical scope and acceptance criteria agreed with the business owner.
Agree on baselines and measure quality, cycle time, manual effort, adoption, and reliability. Report verified outcomes and shared reusable improvements with the engineering team.

Qualifications/Requirements

Bachelor's degree in Computer Science, Software Engineering, Information Systems, Data Engineering, or a related technical field required. Master's or a PhD degree in Computer Science, AI/ML, Data Systems, Information Systems, or a related field preferred.