At a Glance
- Tasks: Conduct innovative research in AI/ML to analyse vast biological data and develop impactful models.
- Company: Join a leading biotech firm focused on cutting-edge life sciences technology.
- Benefits: Attractive salary, health perks, remote work options, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on high-quality code and career advancement.
- Why this job: Make a real difference in healthcare by leveraging AI to solve complex biological challenges.
- Qualifications: PhD or master's in relevant fields with experience in machine learning and software development.
The predicted salary is between 36000 - 60000 £ per year.
Responsibilities
- Carry out product‑driven research on novel machine learning methods to analyze terabytes of internal multi‑modal high‑content data.
- Design approaches to deconvolve real biological signals from confounding effects that are inherent in high‑throughput biological data.
- Leverage internal high performance computing cluster and cloud compute to train and productionize our models at scale.
- Work closely with domain experts on cross‑disciplinary teams to generate actionable insights that impact target identification, hit identification, and safety testing.
- Contribute to our developing codebase with well‑tested, production‑ready code.
Qualifications
- PhD or master’s in computer science, engineering, applied mathematics, machine learning, or equivalent practical experience.
- 2+ years of experience in cell imaging is required for master degree holders.
- 2+ years of experience in machine learning and software engineering best practices.
- 2+ years of experience with working in a collaborative CI/CD software development environment, including use of git.
- 2+ years of experience with developing, implementing, and training deep learning models with PyTorch, Tensorflow, or other deep learning frameworks.
Preferred Qualifications
- Experience working with high‑content imaging and diverse multi‑omics.
- Knowledge in disease biology, molecular biology, and biochemistry.
- Track record of writing software in a team in industrial environments or open‑source projects.
- Track record of projects or peer reviewed publications at the intersection of machine learning and life sciences.
- Mentality of commit early and often, metrics before models, and shipping high quality production code.
EEO Statement
GSK is an Equal Opportunity Employer. This ensures that all qualified applicants will receive equal consideration for employment without regard to race, color, religion, sex (including pregnancy, gender identity, and sexual orientation), parental status, national origin, age, disability, genetic information (including family medical history), military service or any basis prohibited under federal, state or local law.
AI/ML Engineers employer: Gsk
At GSK, we pride ourselves on being an exceptional employer, offering AI/ML Engineers the opportunity to work at the forefront of innovation in a collaborative and inclusive environment. Our commitment to employee growth is reflected in our investment in professional development and access to cutting-edge technology, enabling you to make a meaningful impact in the life sciences sector. Join us in our vibrant location, where diverse teams come together to drive advancements that truly matter.
StudySmarter Expert Advice🤫
We think this is how you could land AI/ML Engineers
✨Tip Number 1
Network like a pro! Reach out to folks in the AI/ML space, attend meetups, and connect on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving deep learning models or high-content imaging. This gives potential employers a taste of what you can do beyond your CV.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Practice explaining complex concepts in simple terms, as you'll likely need to collaborate with domain experts from various fields.
✨Tip Number 4
Don't forget to apply through our website! We love seeing applications directly from candidates who are genuinely interested in joining our team. Plus, it shows you're proactive and keen on being part of our mission.
We think you need these skills to ace AI/ML Engineers
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience in machine learning and software engineering. We want to see how your skills align with the responsibilities listed in the job description, so don’t be shy about showcasing relevant projects!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you’re passionate about AI/ML and how your background makes you a great fit for our team. Keep it concise but impactful – we love a good story!
Showcase Your Collaborative Spirit:Since we work closely with domain experts, it’s important to highlight any collaborative projects you've been part of. Share examples that demonstrate your ability to work in cross-disciplinary teams and how you’ve contributed to successful outcomes.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it gives you a chance to explore more about what we do at StudySmarter!
How to prepare for a job interview at Gsk
✨Know Your Stuff
Make sure you brush up on the latest machine learning methods and frameworks like PyTorch and TensorFlow. Be ready to discuss your past projects, especially those involving high-content imaging or multi-omics data, as this will show your practical experience.
✨Showcase Collaboration Skills
Since the role involves working closely with domain experts, be prepared to share examples of how you've successfully collaborated in cross-disciplinary teams. Highlight any experiences where you contributed to a team project, especially in a CI/CD environment.
✨Prepare for Technical Questions
Expect technical questions that dive deep into your understanding of deep learning models and software engineering best practices. Practise explaining complex concepts in simple terms, as this will demonstrate your ability to communicate effectively with non-technical team members.
✨Demonstrate a Growth Mindset
Emphasise your commitment to continuous learning and improvement. Share instances where you’ve adapted to new technologies or methodologies, and express your enthusiasm for contributing to a developing codebase with high-quality production code.
