At a Glance
- Tasks: Lead innovative ML research projects to revolutionise drug discovery.
- Company: Join a cutting-edge lab focused on transformative machine learning solutions.
- Benefits: Competitive salary, flexible work environment, and opportunities for professional growth.
- Other info: Collaborative and inclusive culture with a focus on diversity in research.
- Why this job: Make a real-world impact by applying ML to solve complex biological challenges.
- Qualifications: PhD or equivalent experience in machine learning with strong technical skills.
The predicted salary is between 60000 - 80000 € per year.
Requirements
- PhD or equivalent practical experience in a technical field
- A proven track record in machine learning using deep learning techniques, including designing new architectures, hands-on experimentation, analysis, and visualisation
- Strong knowledge of linear algebra, calculus and statistics
- Experience using ML frameworks such as JAX, PyTorch, or TensorFlow, and scientific software such as NumPy, SciPy, or Pandas
- A passion for applying ML research to real world problems
- Depending on your experience: project supervision, leadership, or management
- (Desirable) PhD in machine learning or computer science
- (Desirable) Relevant research experience to the position such as post doctoral roles, a proven track record of publications, or contributions to machine learning codebases
- (Desirable) Experience working in a scientific environment across disciplines (particularly biology, chemistry, physics)
- (Desirable) Experience working with biological or chemical data and biological or chemistry software
- (Desirable) Experience working with real-world datasets
- (Desirable) Experience with ML on accelerators
- (Desirable) Experience in any of: large scale deep learning, generative models, graph neural networks, deep learning for drug discovery, deep learning for computer vision, 3D graphics/robotics, real-world applied RL
What the job involves
- As a Research Scientist in machine learning (ML), you will play an exciting role in building greenfield machine learning based models and algorithms that will power our platform to transform the drug discovery world as we know it.
- Working in a highly creative, fast-paced and interdisciplinary environment, you will be partnering with leading engineers and scientists to conceive, design, and develop cutting edge machine learning algorithms to unlock new modelling and predictive power which will be critical to the organisation’s success.
- You will draw upon your existing deep research experience whilst learning from those around you, to apply novel techniques and ideas to newly encountered computational biology and chemistry problems.
- Depending on your experience: You will create and lead projects, bringing together a variety of disciplined scientists and engineers to pursue some of the most ambitious modelling problems with deep learning – as well as providing technical mentorship and people management for others in the ML community at Isomorphic Labs.
- You will be instrumental in leading greenfield machine learning based research projects, building the models, and algorithms that will power our platform to transform the drug discovery world as we know it.
- Contribute to our research directions in machine learning by using your extensive knowledge of the field to apply world-leading ML algorithms to drug discovery.
- Identify and create novel ML techniques and the required data to train.
- Develop the architectures and training algorithms of machine learning models.
- Analyse and tune experimental results to inform future experimental directions.
- Implement and scale training and inference engineering frameworks.
- Report and present research findings and developments clearly and efficiently, to both other ML scientists and scientists of different disciplines.
- Iterate collaboratively with scientists and domain experts, sharing your own domain experience.
- Suggest and engage in team collaborations to meet ambitious research goals.
- Provide technical mentorship and guidance to the ML research community, advising on projects, and shaping our research roadmap based on your deep technical expertise.
- Provide developmental support to other ML research scientists.
- Create, lead, and run ML research projects, fostering collaborative and diverse teams to solve high priority modelling problems.
- Cultivate a diverse and inclusive research culture.
Research Scientist (Machine Learning) in London employer: Deepstreamtech
Isomorphic Labs is an exceptional employer for Research Scientists in Machine Learning, offering a dynamic and interdisciplinary work environment that fosters creativity and innovation. With a strong emphasis on employee growth, you will have the opportunity to lead cutting-edge projects in drug discovery while collaborating with top-tier engineers and scientists. Our commitment to diversity and inclusion ensures a supportive culture where your contributions are valued and your professional development is prioritised.
StudySmarter Expert Advice🤫
We think this is how you could land Research Scientist (Machine Learning) in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend conferences, and join online forums. 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 machine learning projects, especially those using frameworks like PyTorch or TensorFlow. This will give potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Be ready to discuss your past research and how it applies to real-world problems, especially in drug discovery.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are genuinely interested in our mission.
We think you need these skills to ace Research Scientist (Machine Learning) in London
Some tips for your application 🫡
Show Off Your Skills:Make sure to highlight your PhD or equivalent experience in your application. We want to see your proven track record in machine learning, especially with deep learning techniques. Don’t forget to mention any hands-on experimentation and analysis you've done!
Tailor Your Application:When applying, tailor your CV and cover letter to reflect the specific requirements of the Research Scientist role. Use keywords from the job description, like 'machine learning', 'deep learning', and 'real-world problems' to make it clear you’re a perfect fit for us.
Be Passionate:We love candidates who are passionate about applying ML research to real-world problems. Share examples of how your work has made an impact or how you envision your research contributing to drug discovery. Let your enthusiasm shine through!
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team at StudySmarter!
How to prepare for a job interview at Deepstreamtech
✨Know Your Stuff
Make sure you brush up on your machine learning fundamentals, especially deep learning techniques. Be ready to discuss your past projects and how you've applied ML frameworks like JAX, PyTorch, or TensorFlow in real-world scenarios.
✨Show Your Passion
Demonstrate your enthusiasm for applying machine learning to solve real-world problems, particularly in drug discovery. Share specific examples of how your research has made an impact or how you envision it could in the future.
✨Prepare for Technical Questions
Expect to dive deep into topics like linear algebra, calculus, and statistics. Brush up on these areas and be prepared to solve problems on the spot or explain complex concepts clearly and concisely.
✨Collaborative Mindset
Highlight your experience working in interdisciplinary teams. Be ready to discuss how you've collaborated with scientists from different fields and how you can contribute to fostering a diverse and inclusive research culture.
