At a Glance
- Tasks: Build cutting-edge AI models for drug discovery and collaborate with scientists.
- Company: Innovative seed-stage start-up revolutionising drug design with AI.
- Benefits: Competitive salary of £100k plus equity and a chance to shape the future.
- Other info: Opportunity to work in a dynamic environment with high ownership and growth potential.
- Why this job: Join a mission-driven team making real-world impacts in healthcare.
- Qualifications: 3+ years in ML systems, strong software engineering skills, and cloud experience.
The predicted salary is between 100000 - 100000 £ per year.
AI is transforming drug discovery, but there’s a problem. Most models are built on sparse, fragmented, and low-quality data. So instead of accelerating breakthroughs, they often lead to dead ends. We’re working with a cutting-edge, seed-stage start-up building an AI-native platform powered by deeply curated, high-quality experimental molecular data, unlocking better predictions across potency, binding, and ADMET. Their platform is already used by hundreds of chemists globally, directly impacting real-world programs across oncology, neurodegeneration, inflammation, and global health. Now, they’re hiring a Founding Machine Learning Engineer to help define the future of AI-driven drug design.
What you’ll be doing
- Building state-of-the-art models for molecular property prediction, including foundation models and AutoML pipelines
- Designing and scaling ML infrastructure (training pipelines, experiment tracking, model registry, CI/CD)
- Deploying low-latency, production-grade model serving systems
- Developing robust data pipelines for dataset curation, validation, and versioning
- Working closely with scientists, product teams, and users to ship impactful features
What we’re looking for
- 3+ years building and deploying ML systems in production (not just research)
- Strong software engineering fundamentals
- Experience with MLOps tooling, model serving, and containerisation
- Comfortable working with cloud infrastructure (AWS, GCP, or Azure)
- High ownership mindset with the ability to operate in ambiguity
Nice to have
- Background in computational chemistry, physics, or related fields
- Contributions to open-source ML or scientific tooling
- Experience deploying ML systems at scale
If this sounds interesting, even if you do not meet all of the requirements, please apply with your CV attached.
Machine Learning Engineer / Drug Discovery / £100k + Equity in London employer: Few&Far
Join a pioneering seed-stage start-up at the forefront of AI-driven drug discovery, where your contributions will directly influence breakthroughs in global health. With a strong emphasis on collaboration and innovation, we offer a dynamic work culture that fosters personal and professional growth, alongside competitive compensation and equity options. Be part of a mission that not only values your expertise but also empowers you to make a tangible impact in the fields of oncology, neurodegeneration, and inflammation.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer / Drug Discovery / £100k + Equity in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with professionals 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 systems 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 technical interviews by brushing up on your coding skills and ML concepts. Practice common interview questions and work on real-world problems to demonstrate your expertise during the interview process.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Machine Learning Engineer / Drug Discovery / £100k + Equity in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the Machine Learning Engineer role. Highlight your experience in building and deploying ML systems, and don’t forget to mention any relevant projects or contributions to open-source.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you’re passionate about AI in drug discovery. Share specific examples of how your work has made an impact and how you can contribute to our mission of transforming drug design.
Showcase Your Technical Skills:We want to see your technical prowess! Include details about the MLOps tools you've used, your experience with cloud infrastructure, and any relevant programming languages. This is your chance to shine!
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity. We can’t wait to hear from you!
How to prepare for a job interview at Few&Far
✨Know Your Stuff
Make sure you brush up on your machine learning fundamentals and the specific technologies mentioned in the job description. Be ready to discuss your experience with MLOps tooling, model serving, and cloud infrastructure like AWS or GCP. This shows you’re not just a theoretical expert but someone who can apply their knowledge practically.
✨Showcase Your Projects
Prepare to talk about specific projects where you've built and deployed ML systems in production. Highlight any challenges you faced and how you overcame them. This will demonstrate your problem-solving skills and your ability to work in ambiguity, which is crucial for this role.
✨Understand the Company’s Mission
Familiarise yourself with the company’s focus on AI-driven drug discovery and the importance of high-quality data. Being able to articulate how your skills can contribute to their mission will set you apart from other candidates. It shows that you’re genuinely interested in the role and the impact you can make.
✨Ask Insightful Questions
Prepare thoughtful questions about the team dynamics, the technology stack they use, and their vision for the future of AI in drug discovery. This not only shows your enthusiasm but also helps you gauge if the company culture aligns with your values and work style.
