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- Tasks: Develop and optimise AI models for drug discovery using cutting-edge technology.
- Company: Join a pioneering company at the forefront of AI in healthcare.
- Benefits: Enjoy competitive salary, generous equity, private healthcare, and professional growth opportunities.
- Why this job: Be part of impactful research that shapes the future of healthcare with an inclusive culture.
- Qualifications: 5+ years in AI/ML, proficient in Python and deep learning frameworks.
- Other info: Hybrid work model available; apply even if you meet 60-70% of the requirements!
The predicted salary is between 120000 - 200000 £ per year.
Location: London (Hybrid)
Salary: £150-250k base + generous equity
Are you excited to drive AI drug discovery forward by scaling SOTA models and optimizing neural nets on GPUs?
Qualifications
- Bachelor's/ Master's or PhD in Computer Science, Engineering, or a related field
- 5+ years of experience deploying AI/ML models in production settings
- Proficient in Python, C++, and deep learning frameworks like PyTorch or Jax
- Skilled in distributed training (e.g., DDP, FSDP) and model performance optimization
- Experience with GPU architectures, cloud platforms, and hardware tradeoffs
- Familiarity with low-level hardware tuning and custom CUDA kernel development
Key Responsibilities
- Develop cutting-edge AI models for drug discovery using deep learning frameworks
- Scale and optimize distributed training of large AI models on GPUs
- Enhance performance of AI models during both training and inference stages
- Communicate findings through documentation, presentations, and stay updated on AI advancements
Benefits
- Generous stock options
- Competitive base
- Private healthcare
- Generous pension contributions
- Contribute to impactful research at the AI-healthcare frontier
- Access to professional growth via conferences, workshops, and training
- Inclusive, collaborative culture focused on innovation and continuous learning
If you meet 60-70% of the requirements I would love to hear from you.
Senior/ Staff Research Engineer employer: GCS
Contact Detail:
GCS Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior/ Staff Research Engineer
✨Tip Number 1
Make sure to showcase your experience with deploying AI/ML models in production settings during any networking opportunities. Engage with professionals in the field at meetups or conferences, and discuss your past projects to highlight your expertise.
✨Tip Number 2
Familiarise yourself with the latest advancements in AI and drug discovery. Follow relevant research papers and industry news, and be prepared to discuss these topics in detail during interviews to demonstrate your passion and knowledge.
✨Tip Number 3
Connect with current employees at StudySmarter on LinkedIn. Engaging with them can provide insights into the company culture and expectations, and they might even refer you internally, which can significantly boost your chances of landing the job.
✨Tip Number 4
Prepare to discuss specific examples of how you've optimised neural networks and scaled models on GPUs. Being able to articulate your technical skills and problem-solving abilities will set you apart from other candidates.
We think you need these skills to ace Senior/ Staff Research Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in deploying AI/ML models, particularly in production settings. Emphasise your proficiency in Python, C++, and any deep learning frameworks you've worked with, such as PyTorch or Jax.
Craft a Compelling Cover Letter: In your cover letter, express your passion for AI drug discovery and how your skills align with the company's goals. Mention specific projects where you've optimised neural networks or worked with distributed training to showcase your expertise.
Showcase Relevant Projects: Include a section in your application that details relevant projects or research you've conducted. Highlight your experience with GPU architectures, cloud platforms, and any custom CUDA kernel development to demonstrate your technical capabilities.
Prepare for Technical Questions: Anticipate technical questions related to model performance optimisation and distributed training. Be ready to discuss your approach to enhancing AI models during both training and inference stages, as well as any challenges you've faced and how you overcame them.
How to prepare for a job interview at GCS
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Python, C++, and deep learning frameworks like PyTorch or Jax. Bring examples of projects where you've deployed AI/ML models in production settings, as this will demonstrate your hands-on expertise.
✨Understand the Role's Responsibilities
Familiarise yourself with the key responsibilities of the position, such as developing AI models for drug discovery and optimising distributed training on GPUs. This knowledge will help you articulate how your background aligns with their needs.
✨Prepare for Technical Questions
Expect technical questions related to distributed training, model performance optimisation, and GPU architectures. Brush up on these topics and be ready to explain your thought process and problem-solving strategies.
✨Communicate Effectively
Since communication is key in this role, practice explaining complex concepts clearly and concisely. Be ready to discuss your findings and how you stay updated on AI advancements, as this shows your commitment to continuous learning.
