Machine Learning Systems Research Intern at Red Hat working on AI inference and model optimization techniques. Collaborating with experts in the field while gaining hands - on experience in applied ML research.
About the role
- Senior Machine Learning Scientist developing AI-driven solutions for microscopy image analysis in life sciences. Collaborating with cross-functional teams and enhancing predictive analytics with deep learning.
Responsibilities
- Develop and deploy AI – driven solutions for microscopy image analysis and related biotechnology predictions
- Design and optimize architectures leveraging LLMs and VLMs for multimodal data interpretation
- Apply graph theory principles to model biological networks and enhance predictive analytics
- Build and manage multi – agent frameworks for automated data processing and decision making
- Collaborate with cross – functional teams to integrate AI solutions into Sartorius’ bioanalytics ecosystem
Requirements
- Master’s or PhD degree in Computer Science, Machine Learning or related disciplines
- Experience with LLMs (e.g.: GPT, LLaMA) and VLMs (e.g.: CLIP, Flamingo)
- Hands on experience with multi – agent systems and orchestration frameworks
- Familiarity with microscopy imaging and life science data is a plus
- Proficiency in Python and deep learning frameworks (PyTorch, Tensorflow)
- Basic knowledge in graph theory and its applications in AI
- Knowledge of distributed systems and cloud – based AI deployment
- Experience with reinforcement learning and agent – based modeling
- Ability to communicate complex technical concepts to interdisciplinary teams
- You describe yourself as a conceptual and creative thinker with excellent organizational and communication skills (both written and verbal)
- You have the ability to work collaboratively with colleagues within a large, matrix – based organization
- In order to commence working with us, the successful candidate must have the right to work in the UK.
Benefits
- Personal and Professional Development: Mentoring, leadership programs, internal seminar offerings
- Work-life Balance: Remote options, flexible work schedules
- Making an impact right from the start: Comprehensive onboarding, including a virtual online platform – even before joining
- Welcoming Culture: Mutual support, team-spirit and international collaboration; communities on numerous topics such as coaching, agile working and businesswomen network
- Health & Well-Being : Wide section of health and well-being support such as healthcare plans and health assessment services
- Attractive Working Conditions: 37.5 hours working week
- 25 days annual leave, plus public holidays
- Free parking on site
- Annual option to buy, sell or carry forward annual leave
- Free hot and cold drinks
- Regular social events and free exercise classes
- Competitive benefits package, including: Group Personal Pension Plan, Private Medical Insurance, Private Dental Insurance, Group Life Assurance, Group Income Protection, Cycle to work scheme
Job title
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