At a Glance
- Tasks: Develop and enhance AI systems for groundbreaking drug discovery.
- Company: Valence Labs, a pioneering AI research hub within Recursion.
- Benefits: Flexible work environment, competitive salary, and opportunities for professional growth.
- Other info: Diverse and inclusive culture with a focus on collaboration and open science.
- Why this job: Join a passionate team making a real difference in healthcare through innovative ML research.
- Qualifications: PhD or Master's in relevant field with strong programming and machine learning skills.
The predicted salary is between 60000 - 80000 £ per year.
Valence Labs is an AI research and productization engine within Recursion dedicated to industrializing scientific discovery to radically improve lives. Combining the intellectual freedom of academia with the resources and stability of industry, our focus is the development of highly-autonomous systems that will spearhead a fundamental shift in the way treatments are discovered and developed for complex disease. Our research is driven by optimism, purpose, and a shared vision for a healthier tomorrow. We publish in top journals and conferences, are deeply committed to open-science and open-source, and maintain some of the largest and most active research communities in our industry. Our team is located in London and Montreal, where we share close connections with Mila, the world’s largest deep learning research institute.
We’re seeking Research Engineers at all seniority levels 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 industry experience.
- Strong programming skills and understanding of modern software development practices, especially in Python.
- Experience in building and deploying high-performance implementations of deep learning algorithms.
- Proven track record in machine learning, including 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:
- Authorship of publications in peer-reviewed conferences (e.g., NeurIPS, ICML, ICLR, or similar).
- Contribution to high-visibility ML codebases.
- Scientific knowledge of biology, chemistry, or physics along with previous experience working in a scientific environment across disciplines.
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.
Research Engineer - Machine Learning employer: Valence Labs
Valence Labs offers an exceptional work environment that combines the intellectual freedom of academia with the stability and resources of industry, making it a prime employer for those passionate about machine learning and scientific discovery. Located in London, our team thrives in a collaborative culture that values diversity and inclusion, providing ample opportunities for professional growth through engagement with leading research communities and contributions to open-source projects. Join us to be part of a mission-driven organisation dedicated to radically improving lives through innovative AI solutions.
StudySmarter Expert Advice🤫
We think this is how you could land Research Engineer - Machine Learning
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those connected to Valence Labs. Attend meetups, webinars, or conferences related to machine learning and AI. You never know who might have a lead on a job or can put in a good word for you!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving machine learning and software development. Share your work on platforms like GitHub and make sure to highlight any contributions to open-source libraries. 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. Practice coding challenges and be ready to discuss your past projects in detail. Remember, they want to see how you think and approach problems, so be confident and articulate your thought process.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Tailor your application to highlight your experience with distributed machine learning systems and your passion for scientific discovery. Let’s get you that dream job at Valence Labs!
We think you need these skills to ace Research Engineer - Machine Learning
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the Research Engineer role. Highlight your programming skills, machine learning expertise, and any relevant projects or publications to catch our eye!
Craft a Compelling Cover Letter:Your cover letter is your chance to show us your passion for ML and how you can contribute to our mission. Share specific examples of your work and how it relates to drug discovery – we love seeing your enthusiasm shine through!
Showcase Your Projects:If you've worked on any open-source projects or have contributions to high-visibility ML codebases, make sure to mention them! We appreciate candidates who are active in the community and can demonstrate their hands-on experience.
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 us you’re keen on joining our team at Valence Labs!
How to prepare for a job interview at Valence Labs
✨Know Your Stuff
Make sure you brush up on the latest machine learning algorithms and techniques relevant to drug discovery. Valence Labs is all about innovation, so being able to discuss recent advancements or your own projects will show you're not just knowledgeable but also passionate about the field.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced in previous roles and how you tackled them. Use examples that highlight your engineering skills and ability to deploy high-performance ML systems. This will demonstrate your hands-on experience and analytical thinking.
✨Engage with Open Science
Since Valence Labs values open-source contributions, be ready to talk about any projects you've worked on in this area. If you've contributed to high-visibility ML codebases or published in peer-reviewed conferences, make sure to mention these achievements to showcase your commitment to the research community.
✨Communicate Clearly
Practice explaining complex concepts in a simple way. You'll likely need to present your findings and collaborate with interdisciplinary teams, so being able to communicate effectively is key. Consider preparing a brief presentation of your past work to demonstrate your ability to convey scientific content clearly.