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- Tasks: Lead AI/ML model development using biological imaging data for drug discovery.
- Company: Join a pioneering pharmaceutical company focused on integrating AI in early-stage research.
- Benefits: Enjoy flexible work options, competitive salary, and opportunities for professional growth.
- Why this job: Be at the cutting edge of AI in healthcare, making a real impact on patient outcomes.
- Qualifications: PhD in relevant fields with expertise in computer vision and deep learning frameworks required.
- Other info: Collaborate with top academic institutions and industry leaders in a dynamic environment.
The predicted salary is between 43200 - 72000 £ per year.
Our client in the Pharmaceutical Manufacturing industry is seeking a Principal Computer Vision Specialist to lead the development of multiple foundation models using biological imaging data, with the goal of accelerating target and biomarker discovery. This team is building a more integrated AI framework for early-stage research, using cellular imaging as a key modality to power a multi-modal foundation model for their in-vitro, high-throughput screening platform. In this role, you’ll be at the forefront of integrating generative AI into Research & Early Discovery, helping reduce the time from target identification to clinical application.
Day to Day:
- Lead the development and deployment of next-generation AI/ML models using cellular imaging and other biological data types (e.g., molecular, transcriptomics, biomedical literature).
- Define the strategy for applying generative AI in early-stage drug discovery, collaborating with cross-functional teams across biology, chemistry, and data science.
- Stay current with the latest research in computer vision, deep learning, representation learning, and multi-modal data integration.
- Communicate findings through reports, presentations, and scientific publications to both internal and external stakeholders.
- Build and maintain collaborations with academic institutions and industry partners.
Must Haves:
- PhD in Computer Science, Bioinformatics, Computational Biology, Physics, or a related field.
- High level of enterprise experience working in Computer Vision and SME experience in three key fields:
- Foundation models
- Self-supervised learning
- Vision transformers.
Plusses:
- Experience with high-content screening, high-throughput data generation, or single-cell RNA sequencing.
- Familiarity with cloud computing platforms (e.g., AWS, Azure, Nvidia DGX Cloud) for large-scale model training and deployment.
- Knowledge of systems biology, biophysics, or causal inference in computational biology.
- Publication record in top-tier ML/CV conferences (e.g., CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML).
Contact Detail:
Insight Global Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Deep Learning Specialist
✨Tip Number 1
Network with professionals in the pharmaceutical and AI sectors. Attend conferences or webinars related to computer vision and deep learning, as these can provide valuable connections and insights into the industry.
✨Tip Number 2
Stay updated on the latest research and advancements in generative AI and multi-modal data integration. Engaging with recent publications and participating in discussions can help you demonstrate your knowledge during interviews.
✨Tip Number 3
Showcase your hands-on experience with foundation models and programming skills in Python. Consider contributing to open-source projects or creating a portfolio that highlights your work with deep learning frameworks like PyTorch.
✨Tip Number 4
Prepare to discuss your collaborative experiences with cross-functional teams. Highlight specific projects where you successfully integrated AI solutions in biological contexts, as this will resonate well with the role's requirements.
We think you need these skills to ace Deep Learning Specialist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in computer vision, deep learning, and any relevant projects. Emphasise your PhD and any hands-on experience with foundation models and multi-modal data integration.
Craft a Strong Cover Letter: In your cover letter, explain why you are passionate about the role and how your background aligns with the company's goals in pharmaceutical manufacturing. Mention specific experiences that demonstrate your expertise in generative AI and collaboration with cross-functional teams.
Showcase Relevant Projects: Include details of any significant projects you've worked on that relate to cellular imaging or drug discovery. Highlight your contributions and the impact these projects had on the outcomes, especially if they involved high-throughput screening or multi-modal data.
Prepare for Technical Questions: Be ready to discuss your technical skills in Python and deep learning frameworks like PyTorch. Prepare examples of how you've applied self-supervised learning and vision transformers in your work, as well as your approach to integrating generative AI into research.
How to prepare for a job interview at Insight Global
✨Showcase Your Expertise
Be prepared to discuss your experience with foundation models, self-supervised learning, and vision transformers in detail. Highlight specific projects where you've applied these concepts, especially in a biological or pharmaceutical context.
✨Stay Current with Research
Demonstrate your knowledge of the latest advancements in computer vision and deep learning. Mention recent papers or breakthroughs that have influenced your work, showing that you are engaged with the field.
✨Collaborative Mindset
Since this role involves working with cross-functional teams, be ready to discuss how you've successfully collaborated with professionals from different backgrounds, such as biology and chemistry. Share examples of how you’ve communicated complex ideas effectively.
✨Technical Proficiency
Prepare to showcase your programming skills in Python and your familiarity with deep learning frameworks like PyTorch. You might be asked to solve a coding problem or discuss your approach to model training and deployment, so brush up on those skills.