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, competitive salary, and opportunities for professional growth.
- Other info: Join a diverse team passionate about pushing ML boundaries.
- Why this job: Make a real impact on healthcare through innovative machine learning solutions.
- 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 ML Scientist: Bayesian Optimization & Causal Design in London employer: Valence Labs
Valence Labs is an exceptional employer that fosters a collaborative and inclusive work culture, where diverse perspectives are valued and personal needs are prioritised. Employees benefit from engaging in cutting-edge research at the intersection of machine learning and scientific discovery, with ample opportunities for professional growth through interdisciplinary collaboration and contributions to open-source projects. Located in a vibrant area, Valence Labs offers a unique environment for passionate researchers to make a meaningful impact on drug discovery and improve lives globally.
StudySmarter Expert Advice🤫
We think this is how you could land Senior ML Scientist: Bayesian Optimization & Causal Design in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with researchers 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 related to ML and drug discovery. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice explaining complex concepts in simple terms, as you'll need to communicate effectively with both scientists and engineers.
✨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. Plus, it’s a great way to ensure your application gets noticed!
We think you need these skills to ace Senior ML Scientist: Bayesian Optimization & Causal Design in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the role. Highlight your programming skills, ML experience, and any relevant scientific knowledge. We want to see how you can contribute to our mission!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Share your passion for ML and how it relates to drug discovery. Let us know why you're excited about joining StudySmarter and how you can help us improve lives through science.
Showcase Your Projects:If you've worked on interesting ML projects or contributed to open-source libraries, make sure to mention them! We love seeing real-world applications of your skills, so don’t hold back on sharing your achievements.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re serious about joining our team at StudySmarter!
How to prepare for a job interview at Valence Labs
✨Know Your Stuff
Make sure you brush up on your machine learning algorithms and the latest advancements in Bayesian optimisation. Be ready to discuss how you've applied these concepts in real-world scenarios, especially in drug discovery or similar fields.
✨Showcase Your Projects
Prepare to talk about your previous projects, particularly those involving high-throughput bioassay data or deep learning models. Highlight your role, the challenges you faced, and how you overcame them. This will demonstrate your hands-on experience and problem-solving skills.
✨Collaborative Spirit
Since this role involves working with interdisciplinary teams, be ready to share examples of how you've successfully collaborated with scientists from different backgrounds. Emphasise your communication skills and how you’ve contributed to team success.
✨Engage with the Community
If you've contributed to open-source libraries or published research, make sure to mention it! Discussing your involvement in the research community shows your passion for ML and your commitment to advancing the field.
