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- Tasks: Develop cutting-edge AI models for drug discovery and optimise large-scale training on GPUs.
- Company: Join a pioneering company at the forefront of AI in healthcare, based in London.
- Benefits: Enjoy competitive salary, generous equity, private healthcare, and professional growth opportunities.
- Why this job: Be part of impactful research in AI, with a culture that values innovation and collaboration.
- Qualifications: Bachelor's/Master's/PhD in relevant fields with 5+ years of AI/ML experience required.
- Other info: Hybrid work model available; apply even if you meet 60-70% of the qualifications!
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. Click the Easy Apply button!
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
Network with professionals in the AI and healthcare sectors. Attend industry conferences or local meetups to connect with people who work at companies like StudySmarter. This can help you gain insights into the role and potentially get a referral.
✨Tip Number 2
Showcase your projects related to AI drug discovery on platforms like GitHub. Having a portfolio that demonstrates your skills in deploying AI/ML models and optimising performance will make you stand out to us.
✨Tip Number 3
Stay updated on the latest advancements in AI and deep learning frameworks. Follow relevant blogs, podcasts, and research papers to discuss these topics during interviews, showing your passion and commitment to the field.
✨Tip Number 4
Prepare to discuss your experience with distributed training and GPU optimisation in detail. Be ready to share specific examples of challenges you've faced and how you overcame them, as this will demonstrate your expertise to us.
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 Experience: When detailing your work history, focus on your 5+ years of experience and any significant achievements related to GPU architectures, cloud platforms, and model performance optimisation. Use quantifiable results to demonstrate your impact.
Highlight Continuous Learning: Mention any recent conferences, workshops, or training sessions you've attended that relate to AI advancements. This shows your commitment to staying updated in the field and aligns with the company's culture of continuous learning.
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 approach.
✨Communicate Effectively
Since the role involves communicating findings through documentation and presentations, practice explaining complex concepts in a clear and concise manner. This will showcase your ability to convey technical information to both technical and non-technical audiences.
