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Emerald AI

Orchestrates AI workloads under grid stress

Policy & Regulatory Affairs Manager

Full-TimeUpdated on 10/23/2026
No salary listed
Senior
Boston, MA, USA+2 moreMore locations: California, USA | Washington, DC, USA
HybridHybrid work from Washington, D.C., Boston, or the Bay Area, with 2 work-from-home days per week.
ConsultingRisk, Regulatory & Legal Advisory

About the job

Requirements
  • At least 5 years of experience in public policy, government, regulatory affairs, or a related field, such as a government agency, consultancy, think tank, or in-house role.
  • Strong understanding of regulatory processes and stakeholder landscapes.
  • Strong written and verbal communication skills, including the ability to distill complex issues clearly and concisely.
  • Experience drafting policy documents or engaging in regulatory processes.
  • Ability to operate in ambiguity and a fast-paced environment.
  • Ability to work cross-functionally and influence without authority.
  • Experience in sectors relevant to Emerald AI, such as energy, infrastructure, or AI/technology.
Responsibilities
  • Monitor and analyze policy developments across relevant jurisdictions, such as energy, infrastructure, AI, or technology policy.
  • Translate regulatory changes into business impact and provide clear recommendations to internal stakeholders.
  • Support policy strategy development, including identifying risks, opportunities, and engagement priorities.
  • Draft policy materials, including briefing notes, consultation responses, position papers, and executive memos.
  • Engage with external stakeholders, including regulators, policymakers, industry associations, and partners.
  • Coordinate cross-functional alignment with legal, product, operations, and leadership teams.
  • Support advocacy efforts, including meetings, submissions, and public-facing initiatives.
Desired Qualifications
  • Experience working with or before state Public Utility Commissions and/or the Federal Energy Regulatory Commission (FERC).
  • Experience working with or before Independent System Operators/Regional Transmission Organizations (ISO/RTOs), such as PJM or ERCOT.
  • Experience engaging directly before regulators or government officials.
  • Experience at a high-growth company or scaling organization.

About the company

E

Emerald AI

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Website

Emerald AI builds Emerald Conductor software that manages data-center AI workloads by delay tolerance, automatically slowing, pausing, capping, or shifting deferrable tasks when grid stress is detected. It works by ranking workloads by their delay tolerance and applying policy-driven actions to reduce demand during peak or stressed periods. The company differentiates itself by targeting compute itself instead of just cooling, unlocking tens of gigawatts of idle grid capacity—over 100 GW in the U.S.—to support AI growth. Its goal is to coordinate flexible loads in real time to expand usable grid capacity for AI workloads.

Company Size

51-200

Company Stage

Series A

Total Funding

$217.5M

Headquarters

Washington DC, District of Columbia

Founded

2025

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Simplify's Take

What believers are saying

  • August 25, 2026 Series A raised $150 million at a $1.05 billion valuation.
  • Silicon Valley Power's Flexible Load Interconnection Program rewards Emerald's verified flexibility model.
  • Vera Rubin's nearly 100-megawatt Virginia project targets commercialization later in 2026.

What critics are saying

  • PJM and FERC grid-rule fights in 2026 can delay interconnection benefits for years.
  • Customers can reject workload shifting during latency spikes, destroying Emerald's core value proposition.
  • Crusoe, Voltus, Lancium, Schneider Electric, and Eaton can commoditize flexibility software quickly.

What makes Emerald AI unique

  • Emerald Conductor turns AI workloads into dispatchable grid assets, unlike cooling-only efficiency tools.
  • Phoenix tests cut a 256-GPU cluster's base load 25% for three hours.
  • Google, Nvidia, and Anthropic joined AEMA, validating Emerald's grid-flexibility thesis.

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Benefits

  • Health Insurance
  • Dental Insurance
  • Vision Insurance
  • 401(k) Company Match
  • Stock Options
  • Hybrid Work Options

Growth & Insights and Company News

6 month growth

21%

1 year growth

21%

2 year growth

45%
Bloomberg
Sep 18th, 2026
Nvidia, Google, Emerald AI launch alliance to dynamically manage data centre electricity use

Nvidia, Google, and Emerald AI have launched the AI Energy Management Alliance (AEMA), a coalition aimed at dynamically managing electricity use in data centers based on grid conditions. The alliance seeks to optimise power consumption in data centers by responding to real-time energy grid demands. This initiative addresses growing concerns about the substantial energy requirements of AI infrastructure and data center operations. The collaboration brings together major technology companies and energy management expertise to tackle the challenge of balancing computational needs with grid stability and energy efficiency.

Channel Insider
Sep 17th, 2026
Google, Nvidia launch power alliance: AI data centers learn when to use less.

Google, Nvidia launch power alliance: AI data centers learn when to use less. Google, Nvidia, and Emerald AI launched AEMA to help flexible AI data centers secure power faster while reducing pressure on the electrical grid. Sep 17, 2026 Channel Insider content and product recommendations are editorially independent. We may make money when you click on links to our partners. Learn More AI data centers want more power, and Google, Nvidia and Emerald AI are betting they can get it faster by learning when to use less. Google, Nvidia and Emerald AI have launched the AI Energy Management Alliance (AEMA), an 18-member coalition pushing utilities and regulators to treat flexible AI data centers as controllable grid resources. The idea is straightforward: facilities that can reliably cut or shift electricity demand when the grid is strained could have another path to securing the enormous power connections AI infrastructure requires. The approach could change the economics of AI data center development. Instead of waiting solely for utilities to build enough generation and transmission capacity to meet peak demand, developers could trade verifiable power flexibility for faster or larger grid connections. How flexible data centers would work. A data center could respond to grid conditions by shifting or pausing lower-priority computing tasks, drawing from batteries, using on-site generation or reducing consumption during emergencies. AEMA says its framework will focus on measurable performance rather than specific technology. That includes how quickly a facility can respond, how long it can reduce demand, how predictable its response is and how it performs during grid emergencies. The alliance also wants standardized technical requirements and data-sharing rules, along with faster interconnection processes for facilities that make credible and verifiable commitments. "The most effective way to accelerate American AI is to make every data center a good citizen of the grid," said Emerald AI CEO Varun Sivaram. Google already has agreements covering about 1 gigawatt of electricity demand that it can reduce when needed, according to Axios. The power bottleneck. The initiative comes as securing electricity has become a major challenge for new U.S. data centers. Sivaram said in a Fortune commentary that a new facility can wait a decade or more for a grid connection, while the U.S. grid is only about 50% utilized on average. AEMA estimates that making AI facilities moderately flexible during the grid's most stressed periods could unlock up to 100 gigawatts of existing capacity. The companies are also testing the concept. Emerald AI and Nvidia have completed six demonstrations, while Nvidia, Digital Realty and Emerald AI plan to activate a nearly 100-megawatt power-flexible AI facility in Virginia later this year. What changes for data center developers and the channel. The proposal could give data center developers another route to securing power without relying entirely on new generation and transmission projects. Instead of designing every facility around uninterrupted access to electricity at its maximum possible demand, operators could use workload scheduling, batteries, and other power-management tools to reduce consumption when the grid is strained. That could matter as access to electricity becomes a greater constraint on the expansion of AI infrastructure. Nvidia has already moved deeper into the power side of the market, including investing in Cloverleaf Infrastructure as developers compete for land, electricity and grid access. If utilities begin rewarding facilities that can reliably reduce demand, the ability to manage power use by a data center could become another factor in site selection and infrastructure planning, alongside available megawatts, networking, cooling, and access to AI hardware. But the model depends on data centers actually delivering the flexibility they promise. Sivaram told Axios that faster or larger connections should require flexibility that is "verifiable and enforceable." Utilities would need confidence that operators can reduce demand quickly and predictably when called upon, rather than simply promising flexibility to secure a connection. That requirement could also create opportunities across the channel. Data center operators and their customers may need help determining which AI workloads can be delayed or shifted, how much battery capacity is required, when on-site generation should be used, and how power-management systems should respond to utility signals. Energy is already beginning to move into the channel conversation. AppDirect, for example, sees energy procurement as an emerging opportunity for MSPs, resellers, and technology advisors who help customers navigate AI infrastructure decisions. Flexible data centers could widen that opportunity by adding workload orchestration, power monitoring and grid-response planning to the mix. The trade-off is that not every AI workload is equally flexible. Training jobs and other batch computing tasks may offer more room for scheduling changes, while latency-sensitive inference and business-critical applications may be harder to interrupt. Developers will therefore have to demonstrate not only how much electricity they can shed, but which workloads can absorb those reductions without creating problems for customers. That makes regulation and measurement just as important as the underlying technology. AEMA can develop technical standards and demonstrate that flexible computing works, but utilities and regulators ultimately have to decide how those capabilities translate into interconnection agreements and whether flexibility warrants faster or larger grid connections. If that model gains traction, power management could become another layer of the AI infrastructure stack. For channel partners, the question may increasingly shift from simply helping customers find enough computing capacity to helping them build AI environments that can prove when, where, and how they can use less electricity. More Channel Insider news: KKR's Helix targets AI data centers as the AI boom makes access to electricity a growing infrastructure bottleneck. Aminu Abdullahi is a contributing writer for Channel Insider and an B2B technology and finance writer with over 6 years of experience. He has written for various other tech publications, including TechRepublic, eSecurity Planet, IT Business Edge, and more.

Crypto Briefing
Sep 17th, 2026
Google, Nvidia, Anthropic team with Emerald AI for data center grid solutions

Google, Nvidia, Anthropic team with Emerald AI for data center grid solutions. Largest company by market cap on September 30 an hour ago Sponsored: Vera - AI-powered prediction market intelligence, built for serious analysts Explore Vera In a strategic move, Google, Nvidia, and Anthropic have teamed up with Emerald AI to address the challenge of finding additional space for data centers on the power grid. This coalition aims to enhance cloud and AI capabilities by implementing grid-flexible software solutions that can dynamically adjust power usage. The coalition's formation underscores ongoing constraints faced by large AI data-center operators due to grid interconnection and power availability. Emerald AI claims that up to 100 GW of potential capacity could be unlocked on existing U.S. grid infrastructure. Nvidia has already partnered with Emerald AI on grid-flexible AI factories, demonstrating the potential impact of such collaborations on expanding data-center capacity. Key takeaways. * The coalition between Google, Nvidia, and Anthropic with Emerald AI suggests a strategic focus on enhancing AI and cloud capabilities through improved grid flexibility. * Market pricing indicates potential positive sentiment towards Alphabet, though the news is not a direct confirmation of immediate financial impact. * The current market reflects a strong leaning towards Nvidia being the largest company by market cap by the end of September, with Alphabet appearing less likely. What to watch. Watch for developments from the coalition's efforts to integrate grid-flexible solutions, as success could enhance the operational capacity of AI data centers. Updates from Nvidia and Alphabet regarding earnings and strategic advancements in AI and cloud services could influence market perceptions. Additionally, any announcements from other tech giants or geopolitical events affecting the tech industry could further impact market expectations. Disclosure: This article was edited by Estefano Gomez. For more information on how Crypto Briefing create and review content, see its Editorial Policy.

Crypto Briefing
Sep 16th, 2026
Nvidia, Google, and Emerald AI form alliance to advance flexible AI data centers

Nvidia, Google, and Emerald AI form alliance to advance flexible AI data centers. The alliance said flexible data centers can reduce or shift electricity consumption through workload management, energy storage, paired generation and responses to grid contingencies. an hour ago Sponsored: Vera - AI-powered prediction market intelligence, built for serious analysts Explore Vera Emerald AI, Google and NVIDIA announced on Wednesday the formation of the AI Energy Management Alliance (AEMA) to advance flexible AI data centers that can adjust their power consumption in response to grid conditions. The coalition said greater power flexibility could help AI facilities connect to existing infrastructure more quickly while reducing pressure on the grid and supporting energy affordability. According to AEMA, conventional interconnection processes were designed for facilities with relatively stable electricity demand and are less suited to AI data centers that can actively manage their power use. Flexible facilities can move computing workloads, deploy storage, use paired generation and respond to system emergencies, allowing them to function as controllable electricity resources rather than fixed loads. AEMA plans to establish technology-neutral standards based on the actual grid services a facility can provide. The proposed framework will address response speed, duration, predictability and emergency performance, as well as requirements for staying connected during short disruptions, reducing consumption when requested and responding to contingencies. AI, tech, and the markets they move - in one daily briefing. Daily. Free. Join 34,000+ readers across crypto, finance, and policy. The alliance also plans to standardize technical data and create risk-adjusted interconnection pathways for facilities with verifiable flexibility. The coalition will bring together companies and organizations spanning the AI and energy industries, including data center operators, technology providers, power producers, utilities and regional grid operators. Members will work with utilities on interconnection approaches and develop technical, operational and policy frameworks for grid-responsive AI demand. AEMA said Emerald AI and NVIDIA are already developing AI factories with energy and infrastructure partners that can respond to grid conditions in real time. The new alliance aims to broaden those efforts across the US as policymakers and industry participants develop rules for meeting the growing power needs of AI infrastructure. Disclosure: This article was edited by Editorial Team. For more information on how Crypto Briefing create and review content, see its Editorial Policy.

Stav AI
Aug 27th, 2026
Nvidia puts a number on the buildout - and the power bill comes due.

Nvidia puts a number on the buildout - and the power bill comes due. Nvidia's Q2 print: $96.2B revenue, $89B data centre, guidance that skips China. Emerald AI raises $150M to flex the grid to save power. Stav Intelligence Desk August 27, 2026 Yesterday this brief was about a number nobody had yet. Today Stav has it. Nvidia's second-quarter results landed after the bell on 26 August and, once again, the read-through is less about one company's margins than about whether the entire AI capital cycle is still accelerating. It is - and the more interesting signal this quarter is who is paying the power bill. Nvidia's quarter: compute is now revenue, and China is off the map. Nvidia reported $96.2 billion in revenue, up 106% year-on-year and 18% sequentially, with the data centre segment alone at $89.0 billion - up 117% from a year ago. Gross margin held at 75.0% and GAAP diluted EPS came in at $2.46. The line that will get quoted is Jensen Huang's: "compute is revenue." The line that matters more for planning is the guidance - $108 billion for Q3, and explicitly assuming zero data-centre compute revenue from China. Nvidia is now modelling the world's second-largest market as a rounding error and still guiding up double digits. Underneath the headline, two disclosures are worth more than the beat itself. Nvidia said it is standing up third-party financing vehicles with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to mobilise over $500 billion for AI-infrastructure buildout over time - the capital cycle is being moved off balance sheets and onto Wall Street's. And, tucked into the data-centre highlights: Nvidia GPUs with Confidential Computing are now used for confidential inference in Apple's Private Cloud Compute. For anyone who has been told confidential inference is a research demo, it is now in one of the largest consumer privacy systems on earth. Emerald AI raises $150M to make data centres flex. The binding constraint on all of the above is not chips - it is electricity, and that is where the week's most telling raise landed. Emerald AI closed a $150 million Series A at a $1.05 billion valuation on 25 August, co-led by Energize Capital and DCVC, with a roster the company says now includes twelve Fortune Global 500 investors. The pitch is a genuinely different answer to the grid problem: instead of waiting years for new interconnection, make the data centre itself a flexible load that dials power consumption up and down in response to grid conditions - which Emerald argues could unlock more than 100 gigawatts of headroom on the existing US grid. It is not a slide deck. Emerald is working with Digital Realty and Nvidia on a roughly 100-megawatt power-flexible "AI factory" built around the Vera Rubin platform, tested with EPRI, Dominion and the PJM grid operator, and slated to come online later this year. Read alongside Nvidia's $500B financing push, the message is consistent: the industry has decided compute demand is real, and the scramble has moved to the two things that gate it - capital and power. The trust layer goes mainstream. The most under-discussed line in Nvidia's print is a sovereignty story. Confidential computing - inference that runs inside a hardware-attested enclave the operator itself cannot read - has been the missing rung for regulated European buyers who want frontier models without handing plaintext to a US-controlled stack. Apple putting Private Cloud Compute on confidential-computing GPUs is the clearest signal yet that the mechanism is production-grade, not a whitepaper. Nvidia also said it has formed an Open Secure AI Alliance with other vendors to push AI safety and security standards, and continued its run of open model releases for physical AI (Cosmos 3, an open reasoning model for autonomous vehicles, and an open humanoid-robot reference design). The takeaway for platform buyers: the tooling that lets you prove where and how a model ran - not just which model - is maturing fast, and it is exactly the layer sovereignty claims have to be built on rather than asserted. Quick hits. * Europe's compute sovereignty push has a deadline. The EuroHPC Joint Undertaking's AI Gigafactories call - up to seven facilities, with joint public funding acting as anchor customer to unlock €20B+ in private investment - closes 12 November, with selections expected in early 2027. Interest ran hot at the earlier (June 2025) expression-of-interest stage: 76 proposals across 60 sites in 16 member states, ~€230B of indicative investment. (Regulation / Sovereignty) * Autonomy keeps raising. Middle-mile trucking firm Gatik closed a $200M Series D on 25 August, led by the Qatar Investment Authority (with Koch Disruptive Technologies) and joined by ARK Invest - evidence the money is flowing to applied, physical AI as well as the model labs. (Enterprise AI) Stav Intelligence Desk Editorial automation The automated editorial desk behind the Daily Intelligence Brief - AI news that matters for European enterprise, published every weekday morning.