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Kraken Technologies

Cloud-based DERMS enabling real-time energy trading

Head of Applied Research

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeNo salary listedHybrid

About the Job

Requirements

  • Deep expertise in modern machine learning and deep learning, including foundation models, model architectures, and data pipelines.
  • A proven track record of designing, developing, or leading the creation of advanced AI models that delivered measurable technical or commercial outcomes.
  • Strong technical knowledge in several areas, including transformer architectures, foundation-model training and adaptation, representation learning, time-series modelling, reinforcement learning, and related machine-learning techniques.
  • Experience working with large, complex datasets and developing models that identify meaningful patterns, generalise across problems, and operate at scale.
  • A strong understanding of the end-to-end model development lifecycle, including data preparation, training, evaluation, experimentation, inference, and production deployment.
  • Experience building, leading, or technically guiding high-performing teams of machine-learning engineers, research engineers, or applied AI specialists.
  • Ability to translate ambitious technical ideas into clear strategies, practical roadmaps, and systems that create meaningful business value.
  • Strong technical judgement, including evaluating emerging approaches, challenging assumptions, and making pragmatic investment decisions.
  • Ability to communicate complex technical concepts clearly and influence senior engineering, product, and executive stakeholders.
  • Technical expertise in one or more of the following: foundation models, large language models, and transformer-based architectures; large-scale pre-training, fine-tuning, distillation, and model evaluation; time-series foundation models, representation learning, and approaches to structuring and tokenising complex data; reinforcement learning, decision-making models, and learning-based optimisation; distributed model training and modern deep-learning frameworks; scalable data pipelines, training infrastructure, inference systems, and production machine-learning engineering; and model evaluation, experimentation, and techniques for improving performance, reliability, and generalisation.

Responsibilities

  • Partner with the Chief AI Officer to define and execute the applied research strategy and roadmap, identifying foundational capabilities and proprietary technologies that create lasting competitive advantage.
  • Lead development of advanced AI models and architectures to understand and model complex energy systems, behaviour, and decision-making.
  • Explore and develop approaches using foundation models, deep learning, transformer architectures, representation learning, and emerging techniques.
  • Develop generalisable models that support multiple products and applications per client.
  • Establish a technical roadmap balancing ambitious innovation with practical development, scalability, and commercial outcomes.
  • Develop and lead a high-performing team of AI researchers, research engineers, and data scientists.
  • Establish a culture of technological excellence, intellectual curiosity, experimentation, and strong execution.
  • Provide hands-on technical direction, challenge data-modelling and architectural decisions, and help solve complex modelling problems.
  • Attract and develop technical talent, creating an environment where highly capable individuals can do their best work.
  • Set technical priorities, development standards, and measures of success while giving the team autonomy to explore ambitious ideas.
  • Lead the design, development, training, and evaluation of advanced AI models.
  • Develop models that learn underlying patterns in energy demand and system behaviour and can generalise across applications and environments.
  • Advance intelligent decision-making beyond prediction towards reasoning, control, and optimisation.
  • Evaluate new architectures and modelling approaches against existing methods, pursuing state-of-the-art performance in strategically important areas.
  • Ensure models are robust, scalable, and integrable into real-world production environments.
  • Work with engineering, product, and commercial teams to ensure research addresses client needs and can translate into valuable products.
  • Help establish infrastructure, tooling, and development practices to build, evaluate, deploy, and continuously improve sophisticated AI systems.
  • Define measures of model performance and commercial success so technical development remains connected to meaningful outcomes.
  • Apply advanced AI models to real-time multimodal agentic workflows in demanding production environments.
  • Build reusable AI capabilities for multiple products and business opportunities rather than isolated solutions.
  • Act as a senior technical authority shaping strategic decisions about AI technologies, architectures, and investment priorities.
  • Provide technical mentorship and guidance to AI, machine-learning, and engineering teams across the company.
  • Establish high standards for experimentation, model evaluation, and responsible deployment.
  • Monitor AI research and emerging capabilities that could transform software products underpinning the energy industry.
  • Support technical assessments of potential AI partnerships and acquisitions to inform build, buy, and partner decisions.

Desired Qualifications

  • PhD in Machine Learning, Neuroscience, Computer Science, Physics, or a related discipline.
  • Experience developing foundation models or advanced AI systems using large-scale data.
  • Experience with energy systems, electricity markets, demand forecasting, grid flexibility, or other complex physical systems.
  • Experience applying AI to scientific, industrial, or real-world decision-making problems.
  • Experience developing novel model architectures or achieving state-of-the-art results in a commercially relevant domain.
  • Experience establishing a new technical team, capability, or product area from an early stage.

About the Company

Kraken Technologies

Cloud-based DERMS enabling real-time energy trading

KrakenFlex provides a cloud-based DERMS that lets asset owners, energy traders, and retailers monitor and control distributed energy resources in real time. It connects to a wide range of DERs—from storage and generation facilities to residential devices and electric vehicles—and optimizes their output to participate in ancillary and wholesale markets. The product is offered as software-as-a-service (SaaS) with subscription fees (and possibly transaction-based fees for market activities) and is designed for sub-second response times with comprehensive monitoring. Its goal is to help clients maximize value from their energy assets by coordinating DERs across multiple markets from a single platform.

Company size
1,001-5,000
Company stage
Early VC
Total funding
$1B
Headquarters
London, United Kingdom
Founded
2014

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