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 Research Engineer - Multimodal Representation Learning 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. As a Senior Research Engineer, you will have the opportunity to work at the forefront of machine learning in drug discovery, with access to cutting-edge resources and a supportive team of interdisciplinary scientists. The company encourages continuous professional growth through engagement with open-source projects and contributions to high-impact research, making it an ideal environment for those passionate about applying their skills to meaningful scientific advancements.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Research Engineer - Multimodal Representation Learning in London
✨Tip Number 1
Network like a pro! Reach out to folks 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 machine learning and scientific discovery. This gives potential employers a taste of what you can do.
✨Tip Number 3
Don’t just apply; engage! When you find a role that excites you, reach out to the hiring manager or team members. Ask questions about the role and express your enthusiasm for their work.
✨Tip Number 4
Keep learning and sharing! Stay updated with the latest in ML and drug discovery. Write blog posts or share insights on social media to establish yourself as a thought leader in the field.
We think you need these skills to ace Senior Research Engineer - Multimodal Representation Learning in London
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 expertise, 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! Use it to explain why you're passionate about applying ML to real-world problems and how your background aligns with our goals at Valence Labs. Keep it engaging and personal!
Showcase Your Projects:If you've worked on interesting projects, especially those involving multimodal learning or drug discovery, make sure to mention them. We love seeing hands-on experience and innovative solutions, so don’t hold back!
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 shows you’re keen on joining our team!
How to prepare for a job interview at Valence Labs
✨Know Your Stuff
Make sure you brush up on the latest advancements in multimodal representation learning and related machine learning algorithms. Be ready to discuss your previous projects and how they relate to the role, especially any experience with high-throughput bioassay data or scientific environments.
✨Showcase Your Problem-Solving Skills
Prepare to share specific examples of challenges you've faced in your past work and how you tackled them. This could involve discussing your approach to designing and deploying distributed machine learning systems or any innovative methods you've developed for drug discovery.
✨Engage with the Team
Since collaboration is key in this role, think about how you can demonstrate your ability to work with interdisciplinary teams. Be ready to talk about how you've successfully collaborated with scientists from different fields and how that has informed your research.
✨Communicate Clearly
Practice explaining complex concepts in a way that's easy to understand. You might be asked to present your research findings, so being able to communicate effectively through talks or written formats like blog posts will be crucial. Consider preparing a brief presentation on a relevant topic to showcase your communication skills.
