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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates deep expertise in modern machine learning and advanced AI model development, with a strong focus on transformer architectures and scalable data pipelines. Proven ability to lead high-performing teams and translate technical strategies into business value.
Highest-signal resume keywords
Deep Learning ExpertiseTransformer ArchitecturesAI Model DevelopmentTeam LeadershipData Pipeline Management
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningDeep LearningFoundation ModelsReinforcement LearningTime-Series ModellingModel EvaluationData PreparationTraining InfrastructureProduction DeploymentDistributed Model Training
Soft Skills
Technical JudgementCommunication SkillsMentoring
Tools & Technologies
PyTorchJAXScalable Data PipelinesInference Systems
Certifications & Qualifications
PhD in Machine LearningPhD in Computer SciencePhD in NeurosciencePhD in Physics
Industry Keywords
AI ResearchEnergy SystemsModel ArchitecturesCommercial OutcomesTechnical Roadmap
Tech Stack
Tools & technologiesPyTorch
About the role
Key responsibilities & impact- Define and execute Kraken’s applied research strategy and roadmap with the Chief AI Officer
- Lead development of advanced AI models and architectures for complex energy systems, behaviour, and decision-making
- Explore foundation models, deep learning, transformer architectures, representation learning, and emerging techniques
- Develop generalisable models supporting multiple products and applications per client
- Establish a technical roadmap balancing innovation, scalability, practical development, and commercial outcomes
- Build and lead a high-performing team of AI researchers, research engineers, and data scientists
- Establish a culture of technological excellence, experimentation, and execution
- Provide hands-on technical direction on data modelling and architectural decisions
- Lead design, development, training, and evaluation of proprietary AI models
- Develop models for energy demand, system behaviour, reasoning, control, and optimisation
- Ensure models are robust, scalable, and integrated into production environments
- Partner with engineering, product, and commercial teams to turn research into products and measurable client value
- Establish infrastructure, tooling, and development practices for building, evaluating, deploying, and improving AI systems
- Apply advanced AI models to real-time multimodal agentic workflows
- Serve as a senior technical authority on AI technology, architecture, and investment priorities
- Mentor AI, machine learning, and engineering teams across Kraken
- Establish standards for experimentation, model evaluation, and responsible deployment
- Monitor AI research developments and support technical assessments of partnerships and acquisitions
Requirements
What you’ll need- Deep expertise in modern machine learning and deep learning
- Strong understanding of foundation models, model architectures, and data pipelines
- Proven track record designing, developing, or leading advanced AI models with measurable technical or commercial outcomes
- Strong technical knowledge of transformer architectures, foundation-model training and adaptation, representation learning, time-series modelling, reinforcement learning, and related machine learning techniques
- Experience with large, complex datasets and models operating at scale
- 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 machine learning, research engineering, or applied AI teams
- Ability to translate technical ideas into strategies, roadmaps, and systems creating business value
- Strong technical judgement and ability to evaluate emerging approaches
- Ability to communicate complex technical concepts to senior engineering, product, and executive stakeholders
- Expertise in areas including foundation models, large language models, transformer architectures, large-scale pre-training, fine-tuning, distillation, model evaluation, time-series foundation models, representation learning, reinforcement learning, distributed model training, PyTorch or JAX, scalable data pipelines, training infrastructure, inference systems, and production ML engineering
- PhD in Machine Learning, neuroscience, Computer Science, Physics, or related discipline is a bonus, not required
- Applicants must have the right to work in the country of application; visa sponsorship is unavailable
Benefits
Comp & perks- Accommodation requests supported during the hiring process
- Equal opportunity employment and non-discrimination
- No visa sponsorship; applicants must have the right to work in the country of application