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 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 research, particularly in drug discovery. Located in a vibrant area, our team enjoys a dynamic environment that encourages creativity and the pursuit of meaningful work aimed at improving lives.
StudySmarter Expert Advice🤫
We think this is how you could land Senior ML Scientist: Bayesian Optimization & Causal Design
✨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 discovery. 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 common ML scenarios and be ready to discuss your past experiences in detail.
✨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 proactive!
We think you need these skills to ace Senior ML Scientist: Bayesian Optimization & Causal Design
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the role. Highlight your expertise in machine learning, programming, 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 ML and how your background makes you a perfect fit for the team. Don’t forget to mention any experience with drug discovery or interdisciplinary collaboration.
Showcase Your Projects:If you've worked on any interesting projects, especially those involving ML or scientific data, make sure to include them. We love seeing hands-on experience, so share your contributions to open-source libraries or any publications you've authored.
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. Plus, we’re excited to see what you bring to the table!
How to prepare for a job interview at Valence Labs
✨Know Your Stuff
Make sure you brush up on your machine learning algorithms and distributed systems. Be ready to discuss your previous projects in detail, especially those that relate to drug discovery or scientific data. This shows you’re not just familiar with the theory but have practical experience too.
✨Showcase Your Problem-Solving Skills
Prepare to tackle some real-world problems during the interview. Think of examples where you've had to solve complex issues using ML techniques. This will demonstrate your analytical thinking and how you approach challenges in a scientific context.
✨Communicate Clearly
Since you'll be collaborating with interdisciplinary teams, practice explaining your research and findings in simple terms. Being able to communicate complex ideas clearly is crucial, so consider how you would present your work to someone without a technical background.
✨Be Passionate and Curious
Let your enthusiasm for ML and its applications shine through. Talk about recent advancements in the field that excite you, or share your thoughts on future trends. This not only shows your passion but also your commitment to staying updated in a rapidly evolving area.
