At a Glance
- Tasks: Lead the development of AI systems to revolutionise scientific discovery.
- Company: Valence Labs, a pioneer in ML for drug discovery.
- Benefits: Inclusive culture, diverse team, and opportunities for impactful research.
- Other info: Collaborate with top scientists and contribute to groundbreaking research.
- Why this job: Make a real difference in healthcare through innovative machine learning.
- Qualifications: PhD or Master's with 3+ years in ML and strong programming skills.
The predicted salary is between 60000 - 80000 £ per year.
We’re seeking an experienced Research Engineer to shape and lead the development of software and AI systems that will help in our mission of industrializing scientific discovery to radically improve lives. We're looking for individuals with strong engineering skills, including expertise in designing, implementing, improving, and deploying distributed machine learning systems at significant scale. In addition, we highly value proficiency with state-of-the-art machine learning algorithms and exceptional problem solving skills.
In this role, you will:
- Support Valence Labs’ research agenda across ML for drug discovery.
- Engage with and contribute to open-source libraries developed by Valence and the research community.
- Create and improve novel ML methods that will accelerate drug discovery.
- Collaborate with an interdisciplinary team of dry and wet lab scientists to inform and improve our models and systems.
- Present and communicate research findings through talks, blog posts, publications, and conferences.
A successful candidate will have most of the following:
- PhD or Master's degree with 3+ years of industry experience.
- Strong programming skills and understanding of modern software development practices, especially in Python.
- Scientific knowledge of biology, chemistry, or physics along with previous experience working in a scientific environment across disciplines.
- Experience with real world natural science data and the associated challenges.
- Experience with high throughput bioassay data such as next-generation sequencing data, cellular imaging data, proteomics data or similar.
- Proven track record in machine learning, including multimodal learning, designing new architectures, hands-on experimentation, analysis, visualization, and model deployment.
- Demonstrated capability to understand and summarize scientific content and implement deep learning models based on descriptions from publications.
- Strong knowledge of linear algebra, calculus, and statistics.
- Passion for applying ML research to real-world problems.
Nice to have:
- Experience in building and deploying high-performance implementations of deep learning algorithms.
- Authorship of publications in peer-reviewed conferences (e.g., NeurIPS, ICML, or ICLR) or journals (e.g. Nature, Science, JACS, or ACS).
- Contribution to high-visibility ML codebases.
Valence Labs is committed to creating a diverse and inclusive environment, where understanding and accommodating personal needs and preferences is a priority. Join our multidisciplinary team of passionate researchers, eager to push the boundaries of ML research and contribute to industrializing scientific discovery to radically improve lives.
Senior Research Scientist - Bayesian Optimization / Experimental Design or Causal Machine Learning employer: Valence Labs
Valence Labs is an exceptional employer that fosters a collaborative and inclusive work culture, where innovation thrives and diverse perspectives are valued. With a strong commitment to employee growth, we offer opportunities for professional development through engaging projects in cutting-edge machine learning and drug discovery. Located in a vibrant area, our team enjoys a dynamic environment that encourages creativity and the pursuit of meaningful scientific advancements.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Research Scientist - Bayesian Optimization / Experimental Design or Causal Machine Learning
✨Tip Number 1
Network like a pro! Reach out to people in your field on LinkedIn or at conferences. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to ML and scientific research. This gives 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. Practice explaining complex concepts in simple terms – it’s all about communication!
✨Tip Number 4
Don’t forget to apply through our website! We love seeing candidates who are genuinely interested in joining our mission at Valence Labs. It’s a great way to stand out!
We think you need these skills to ace Senior Research Scientist - Bayesian Optimization / Experimental Design or Causal Machine Learning
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the job description. Highlight your programming skills, machine learning experience, and any relevant scientific knowledge to show us you're the right fit.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about applying ML to real-world problems. Share specific examples of your work in drug discovery or related fields to grab our attention!
Showcase Your Projects:If you've contributed to open-source libraries or have publications, make sure to mention them! We love seeing your hands-on experience and how you've engaged with the research community.
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 don’t miss out on any important updates from our team!
How to prepare for a job interview at Valence Labs
✨Know Your Stuff
Make sure you brush up on your knowledge of Bayesian optimisation, experimental design, and causal machine learning. Be ready to discuss specific algorithms and methods you've worked with, as well as how they can be applied to drug discovery.
✨Showcase Your Projects
Prepare to talk about your previous projects, especially those involving high-throughput bioassay data or real-world natural science data. Highlight your role in these projects and the impact they had, demonstrating your problem-solving skills and technical expertise.
✨Collaborate and Communicate
Since this role involves working with interdisciplinary teams, think of examples where you've successfully collaborated with others. Be ready to discuss how you communicate complex scientific concepts to non-experts, as this will show your ability to engage with diverse team members.
✨Passion for the Field
Express your enthusiasm for applying machine learning to real-world problems. Share any relevant publications or contributions to open-source libraries, as this will demonstrate your commitment to advancing the field and your proactive approach to research.
