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- Tasks: Drive innovative projects in drug discovery using AI and cheminformatics.
- Company: Join SandboxAQ’s AI Simulation group, revolutionising drug discovery with cutting-edge technology.
- Benefits: Enjoy competitive salary, potential bonuses, and equity options.
- Why this job: Be part of a dynamic team tackling real-world challenges in healthcare and science.
- Qualifications: PhD in relevant fields with 1-5 years of experience in machine learning and computational chemistry.
- Other info: Contribute to scientific publications and present at conferences.
The predicted salary is between 100000 - 140000 £ per year.
Machine Learning Cheminformatics Engineer, Drug Discovery (EMEA) SandboxAQ’s AI Simulation group partners with global research teams to discover new drugs and materials using AI and physics-based computational solutions. We are seeking an experienced researcher to drive innovative and impactful projects leveraging cheminformatics, machine learning, and computational chemistry for drug discovery. The successful candidate will demonstrate strong abilities in cheminformatics and/or bioinformatics, including knowledge of established techniques and cutting-edge machine learning methods for modeling molecular properties and interactions with complex systems. They will also have experience with scientific programming and data science. These skills will be leveraged within a seasoned, agile, and multi-disciplinary group, including drug hunters with an excellent track record in drug discovery, computational chemists, physicists, AI experts, and software engineers. Design and implement software that leverages informatics, machine learning, and computational chemistry to address unmet needs in drug discovery Leverage Bayesian optimization and active learning to improve experimental designs and make data-driven decisions Translate research and applications to maintainable software systems Contribute to the scientific community by writing patents / journal articles and presenting at conferences Translate insights from statistics, multimodal data analysis, and ML to actionable and testable drug discovery hypothesis PhD in chemistry, biology, computer science, or a related discipline ~1-5 years of relevant experience including hands-on experience with informatics, machine learning, and computational chemistry applied to drug discovery in the private sector, like biotech or pharma ~ Experience with molecular property prediction and multi-objective optimization using machine learning and / or deep learning methods ~ Experienced with common python toolkits for scientific computing (e.g., numpy, pandas, scipy), machine learning (e.g., Familiarity running simulations and training models on high-performance computing (GPU) environments for corporate R&D, innovation labs, or academic research ~ An interest in solving scientific problems in chemistry and biology via computational and data-driven methods ~ Hands-on mentality & comfortable with getting deep into the technical weeds of highly complex problems, and a track record of driving projects to completion The US base salary range for this full-time position is expected to be $142k – $198k per year. Within the range, individual pay is determined by factors including job-related skills, experience, and relevant education or training. This role may be eligible for annual discretionary bonuses and equity. #
Machine Learning Performance Engineer employer: Le Lab Quantique
Contact Detail:
Le Lab Quantique Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Performance Engineer
✨Tip Number 1
Familiarise yourself with the latest advancements in cheminformatics and machine learning. Follow relevant journals, attend webinars, and engage with online communities to stay updated on cutting-edge techniques that are being applied in drug discovery.
✨Tip Number 2
Network with professionals in the biotech and pharmaceutical sectors. Attend industry conferences and workshops where you can meet potential colleagues and learn about their experiences. This can provide valuable insights and may even lead to referrals.
✨Tip Number 3
Showcase your hands-on experience with scientific programming and data science. Engage in projects or contribute to open-source initiatives that demonstrate your skills in Python and machine learning toolkits, as this will make you stand out to hiring managers.
✨Tip Number 4
Prepare to discuss specific examples of how you've applied machine learning and computational chemistry in previous roles. Be ready to explain your thought process and the impact of your work, as this will highlight your problem-solving abilities and technical expertise.
We think you need these skills to ace Machine Learning Performance Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in cheminformatics, machine learning, and computational chemistry. Use specific examples from your past work that demonstrate your skills in these areas.
Craft a Compelling Cover Letter: In your cover letter, express your passion for drug discovery and how your background aligns with the role. Mention any specific projects or achievements that showcase your expertise in machine learning and informatics.
Showcase Technical Skills: Clearly outline your proficiency with Python toolkits and any experience with high-performance computing environments. Include details about your hands-on experience with molecular property prediction and multi-objective optimization.
Highlight Collaborative Experience: Since this role involves working within a multi-disciplinary team, emphasise any previous collaborative projects. Discuss how you contributed to team success and how you can bring that experience to the new role.
How to prepare for a job interview at Le Lab Quantique
✨Showcase Your Technical Skills
Be prepared to discuss your experience with cheminformatics, machine learning, and computational chemistry in detail. Highlight specific projects where you've applied these skills, especially in drug discovery, and be ready to explain the methodologies you used.
✨Demonstrate Problem-Solving Abilities
Expect questions that assess your problem-solving skills. Prepare examples of complex problems you've tackled in previous roles, particularly those involving data-driven decisions or experimental designs using Bayesian optimisation and active learning.
✨Familiarise Yourself with Relevant Tools
Make sure you're comfortable discussing common Python toolkits like NumPy, Pandas, and SciPy. Be ready to explain how you've used these tools in scientific computing and machine learning, as well as any experience with high-performance computing environments.
✨Prepare for Collaborative Discussions
Since this role involves working within a multi-disciplinary team, be ready to talk about your collaborative experiences. Share examples of how you've worked with chemists, biologists, or software engineers to drive projects forward and achieve common goals.
