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For Companies At a Glance
- Tasks: Join a dynamic team to apply machine learning in drug discovery and tackle complex scientific challenges.
- Company: Be part of an innovative startup transforming AI research into real-world pharmaceutical solutions.
- Benefits: Enjoy hybrid work flexibility, cutting-edge technology, and the chance to make a real impact.
- Why this job: Contribute to groundbreaking advancements in healthcare while working in a collaborative and passionate environment.
- Qualifications: Master’s or Ph.D. in relevant fields with experience in machine learning frameworks and programming.
- Other info: Exciting opportunity for those eager to drive change in the biotechnology industry.
The predicted salary is between 48000 - 72000 £ per year.
We are looking for a highly skilled and motivated Machine Learning Engineer to join this exciting start up and help translate cutting-edge AI research into real-world impact in drug discovery. If you are passionate about applying machine learning to solve complex scientific challenges, this is an exciting opportunity to make a meaningful contribution to the pharmaceutical and biotechnology industries.
What You Will Do
- Work closely with cross-functional teams to integrate machine learning models into impactful scientific applications.
- Analyse large-scale biological and chemical datasets to uncover patterns and generate actionable insights.
- Develop a highly scalable machine learning platform to support the integration of advanced AI models into the drug discovery process.
- Design, build, and maintain scalable ML infrastructure to handle high-throughput data processing efficiently.
- Continuously evaluate and refine model performance, ensuring accuracy, reliability, and real-world applicability.
- Stay at the forefront of AI advancements in drug discovery, bringing the latest innovations into your work.
Requirements
- A Master’s or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Cheminformatics, or a related field.
- Proven experience with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.
- Industry experience in pharmaceuticals, biotechnology, or scientific discovery.
- Strong programming skills in Python for machine learning and data processing.
- Familiarity with big data technologies such as Spark, Kafka, and Iceberg.
- Knowledge of data modelling, database design, and data warehousing concepts.
- Experience with data visualisation tools like Superset, Grafana, or Metabase.
- Strong problem-solving skills with the ability to work independently and as part of a collaborative team.
- Excellent organisational and time-management skills.
- A deep passion for drug discovery and a drive to make a real impact in the field.
If you are excited by the opportunity to apply machine learning to accelerate breakthroughs in drug discovery, we would love to hear from you. Get in touch to discuss the opportunity in more detail. Alternatively, please send over your CV and, if suitable, the KEMIO team will be in contact to organise a conversation!
Apply now and be part of something extraordinary!
Machine Learning ML Engineer - Hybrid - London employer: Kemioconsulting
Contact Detail:
Kemioconsulting Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning ML Engineer - Hybrid - London
✨Tip Number 1
Network with professionals in the pharmaceutical and biotechnology sectors. Attend industry conferences, webinars, or local meetups to connect with people who can provide insights into the role and potentially refer you.
✨Tip Number 2
Showcase your practical experience with machine learning frameworks by working on relevant projects. Contributing to open-source projects or creating a portfolio of your work can demonstrate your skills effectively.
✨Tip Number 3
Stay updated on the latest advancements in AI and drug discovery. Follow key publications, blogs, and thought leaders in the field to discuss these topics during interviews, showing your passion and knowledge.
✨Tip Number 4
Prepare for technical interviews by practising coding challenges and machine learning problems. Use platforms like LeetCode or HackerRank to sharpen your skills, focusing on Python and data processing techniques.
We think you need these skills to ace Machine Learning ML Engineer - Hybrid - London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning, particularly with frameworks like TensorFlow or PyTorch. Emphasise any industry experience in pharmaceuticals or biotechnology, as this will be crucial for the role.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for drug discovery and how your skills can contribute to the company's mission. Mention specific projects or experiences that demonstrate your problem-solving abilities and technical expertise.
Highlight Technical Skills: Clearly list your programming skills, especially in Python, and any familiarity with big data technologies like Spark or Kafka. This will help the hiring team see your fit for the technical demands of the role.
Showcase Collaborative Experience: Since the role involves working closely with cross-functional teams, include examples of past collaborative projects. Highlight your ability to work independently as well as part of a team, which is essential for success in this position.
How to prepare for a job interview at Kemioconsulting
✨Showcase Your Technical Skills
Be prepared to discuss your experience with machine learning frameworks like TensorFlow, PyTorch, or scikit-learn. Bring examples of projects where you've applied these technologies, especially in the context of drug discovery or related fields.
✨Demonstrate Problem-Solving Abilities
Expect to face technical challenges during the interview. Practice explaining your thought process when tackling complex problems, particularly those related to data analysis and model performance evaluation.
✨Highlight Collaborative Experience
Since the role involves working closely with cross-functional teams, be ready to share examples of how you've successfully collaborated with others. Discuss any experiences where you integrated machine learning models into scientific applications.
✨Stay Updated on AI Advancements
Research the latest trends and innovations in AI, particularly in drug discovery. Being able to discuss recent advancements will show your passion for the field and your commitment to staying at the forefront of technology.
