4 часа назад
Machine Learning Engineer (AI)
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Machine Learning Engineer (AI): Architecting and deploying AI systems for the multi-scale design of physical technologies, including foundation models for atomic motion, liquid-flow simulation, computational chemistry, and agentic workflows with an accent on production-scale ML engineering and scientific applications. Focus on designing novel ML architectures, training and evaluating models, driving research from conception to deployment, and building robust systems for complex industrial challenges.
Location: London, UK; hybrid work
Company
is an AI-first industrial company developing hardware and materials for more performant and sustainable AI data centers.
What you will do
- Architect robust AI systems for the multi-scale design of physical technologies.
- Set standards for code quality, system architecture, ML research, and engineering practices through hands-on coding and technical reviews.
- Make technical decisions on model selection, training approaches, and deployment strategies.
- Develop and deploy AI solutions across computational chemistry simulations, agentic workflows, and the broader technology development pipeline.
- Design novel ML architectures for complex scientific domains and drive research projects from conception through production deployment.
- Collaborate closely with scientists and engineers while rapidly developing expertise in new technical areas.
Requirements
- Significant software engineering and machine learning experience, including training, evaluating, and deploying AI models.
- Experience productionising AI models at scale and understanding the full ML lifecycle from research to deployment.
- Strong software engineering fundamentals and the ability to architect robust, maintainable systems.
- Strong reasoning skills in algorithms, system design, linear algebra, probability, and ML engineering trade-offs.
- Ability to debug complex ML systems, test edge cases, and design carefully selected ablations.
- Genuine interest in AI systems for scientific and industrial applications.
Nice to have
- Experience with physics-informed or chemistry-focused AI applications.
- Experience building or fine-tuning large language models.
- Experience with agent-based systems, tool use, or agentic workflows.
- Open-source ML contributions or published research.
Culture & Benefits
- Work in tightly integrated, vertically integrated teams spanning AI research, operations, materials, engineering, and manufacturing.
- Emphasis on craftsmanship, continual learning, low ego, and high technical standards.
- Opportunity to apply AI to major industrial technology challenges.
- Inclusive environment and equal-opportunity employment.
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