Director of Industry Solutions Implementation

GridCARERedwood City

On-site Full-time $175K/yr - $250K/yr

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Experience Level

Senior Level Manager

Qualifications

QualificationsCandidates should possess a robust background in energy solutions, AI, and project implementation. Proven experience in managing complex projects and stakeholder engagement. Strong analytical and problem-solving skills. Excellent communication and leadership abilities. Ability to thrive in a fast-paced, innovative environment.

About the job

Join GridCARE as the Director of Industry Solutions Implementation

At GridCARE, we are revolutionizing the future of AI by tackling one of its most pressing challenges: immediate access to power. In an era where computing demands are soaring, our innovative physics-based generative AI platform reveals untapped energy capacity within the existing electric grid. This empowers hyperscalers, data center developers, and utilities to advance AI infrastructure far more efficiently than traditional methods, without incurring substantial upgrade costs.

Established at Stanford’s Doerr School of Sustainability and supported by prominent investors in climate and deep technology, GridCARE is home to a distinguished team of experts in power systems, AI, and infrastructure.

As the Director of Industry Solutions Implementation, you will:

Operate at the nexus of AI, energy, and infrastructure, driving the next industrial revolution.
Collaborate with hyperscalers, developers, and utilities on transformative, real-world projects.
Contribute to a more abundant, efficient, and resilient energy future in the digital age.
Be part of a pioneering team creating a new category in capacity acceleration for AI.
Enjoy competitive compensation, equity, and benefits in a dynamic, mission-driven environment.

Discover more about GridCARE:

About GridCARE

GridCARE is at the forefront of the AI revolution, dedicated to solving the energy access challenges that define the industry's future. With a strong foundation from Stanford and robust backing from key investors, we are assembling a talented team to unlock the hidden potential of the electric grid.

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About Hammerhead AIAt Hammerhead AI, we are pioneering the future of artificial intelligence through innovative orchestration, tackling one of the most critical barriers to AI's power accessibility. Our state-of-the-art platform enhances data center power infrastructure to maximize AI token generation within existing electrical constraints, eliminating the need for new power plants or grid extensions. With our team already optimizing over 8 gigawatts of mission-critical power worldwide, we are seizing a $64 billion per year market opportunity while significantly reducing the environmental impact of AI infrastructure.As part of our dynamic team, you will: Engage at the confluence of AI, energy, and computing to create the next generation of AI infrastructure Collaborate with experts in modern reinforcement learning, AI, IoT, and IIoT software, as well as infrastructure technologies Play a vital role in fostering a more efficient and sustainable future for AI computation. Join a company at the forefront of advanced data center design and operations Enjoy competitive compensation, equity, and benefits in a thriving, mission-driven environment. Learn from a seasoned team with a proven track record of building and selling successful startups.Role OverviewAs a Reinforcement Learning Engineer, you will be the architect behind the core intelligence of Hammerhead’s ORCA platform. Reporting directly to the Head of AI/Reinforcement Learning Engineering, you will be responsible for designing, training, and deploying Orchestrated RL Control Agents that serve as the brain of our system, enabling real-time optimization of power and computational resources across physical data centers. This position is tailored for a hands-on expert passionate about translating advanced RL research into practical applications for complex industrial systems. You will play a crucial role in developing models to control physical assets such as cooling systems and power distribution units, driving substantial efficiency improvements in AI workloads.Key ResponsibilitiesDesign, train, and implement reinforcement learning algorithms for power and compute resource optimization.Develop and maintain RL models that interact with physical data center assets.Collaborate with cross-functional teams to integrate RL solutions into existing infrastructure.Continuously monitor and enhance the performance of deployed RL models.Stay abreast of the latest developments in reinforcement learning and AI technologies.

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