Member of Technical Staff - Research

We’re at a pivotal moment for AI and energy. Demand for compute is skyrocketing, but power constraints are becoming a critical bottleneck. Emerald AI sits at the intersection of these two worlds, enabling AI data centers to scale without overwhelming the grid.

Our Emerald Conductor software platform makes data centers flexible and responsive, allowing them to adjust power usage dynamically. This unlocks massive AI growth without major new infrastructure, while also strengthening the grid and supporting the expansion of renewable energy.

We’re a team of experts across AI, cloud, software, and energy—on a mission to scale AI sustainably. We’re backed by leading investors and partners including Radical Ventures and NVIDIA.

Learn more about our vision, team, and backers at https://www.emeraldai.co/.

As a Member of Technical Staff, you will help invent and build the next generation of software systems that enable AI infrastructure to operate intelligently under power constraints. You will work at the intersection of AI systems, distributed computing, cloud infrastructure, and energy-aware optimization, developing technologies that move from research prototypes into production deployments.

This is a highly technical role for researchers who enjoy building real systems. We are looking for individuals who combine strong research skills with hands-on software engineering experience and are excited to develop production-quality infrastructure, large-scale prototypes, and experimental platforms that operate on thousands of GPUs.

You will collaborate closely with your team mates, product, and customer teams to translate new ideas into deployed capabilities while publishing cutting-edge research that advances the state of the art in AI infrastructure.

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  Design and build novel systems for power-aware AI infrastructure, distributed computing, and large-scale cloud platforms.

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  Develop production-quality software, research prototypes, and experimental infrastructure that can be deployed in real-world AI data centers.

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  Apply machine learning, optimization, systems, or control techniques to challenging problems in AI infrastructure and cloud operations.

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  Design, implement, and evaluate algorithms using large-scale experimental platforms and production deployments.

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  Partner with product and customer facing teams to transition research innovations into customer-facing products.

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  Collaborate with partners across industry and academia on cutting-edge research initiatives.

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  Publish high-impact research in leading systems and AI conferences when appropriate.

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  Help shape Emerald AI's long-term technical roadmap and identify new research directions with commercial impact.

• Ph.D. in Computer Science, Computer Engineering, Electrical Engineering, or a closely related field.

• Strong background in one or more of the following:

  • Machine learning systems

  • AI infrastructure

  • Distributed systems

  • Cloud computing

  • Systems for AI or HPC

  • Performance optimization

• Excellent software engineering skills with experience developing large software systems in languages such as C++, Python, Go, or Rust.

• Experience building research prototypes or with large-scale production code.

• Strong publication record or demonstrated history of delivering impactful technical innovations.

• Ability to independently drive research from idea through implementation and evaluation.

• Experience deploying systems in production cloud or distributed environments.

• Experience working with large codebases, production software, or open-source infrastructure.

• Experience with Kubernetes, Slurm, distributed training/inference frameworks, or large-scale AI infrastructure.

• Experience with GPU systems, accelerators, or performance analysis tools.

• Experience in optimization, control systems, resource scheduling, or systems performance.

• Experience with power management, energy-efficient computing, sustainability, or data center infrastructure.

• Experience taking research innovations from prototype to production.