At a Glance
- Tasks: Develop deep-learning pipelines and enhance model usability for groundbreaking research.
- Company: Leading research organisation in London focused on innovative machine learning.
- Benefits: Competitive salary, collaborative environment, and opportunities for professional growth.
- Other info: Dynamic role with mentorship opportunities and a focus on interdisciplinary collaboration.
- Why this job: Join a pioneering team and contribute to cutting-edge research in drug discovery.
- Qualifications: Relevant degree and experience in machine learning, Python, and scientific software.
The predicted salary is between 50000 - 70000 € per year.
We are currently looking for a Machine Learning Engineer to join a leading research organisation based in London. As Machine Learning Engineer, you will be responsible for advancing data-processing, model-building, and deployment capabilities for a pioneering research organisation.
KEY DUTIES AND RESPONSIBILITIES:
- Develop deep-learning pipelines for nuclear magnetic resonance (NMR) data and innovative machine learning approaches to elucidate and quantify interactions between small molecules and intrinsically disordered proteins.
- Enhance the usability of built models by implementing automated, streamlined, and efficient software solutions in line with best practices, and build model-deployment and job-launching systems for internal and external use.
- Collaborate closely with other computational and NMR team members, in addition to experimental biophysicists, assisting with experimental data handling and curation, and mentoring the interdisciplinary team in machine-learning and data analysis methods.
- Stay current with breakthroughs in machine learning, neural networks, NMR, and computational technologies, contributing to the design and execution of cutting-edge machine learning and NMR research projects.
ROLE REQUIREMENTS:
- Relevant degree in a technical field with proven experience in machine learning or model-building.
- Proven industry experience in applying machine learning and modelling techniques to graph-based data such as molecules and proteins, as well as time series. An ability to demonstrate innovative ways of working (for example work on disordered proteins and consideration of the next frontier in drug discovery) will be highly advantageous.
- A working knowledge and practical experience with Python, and extensive experience with the scientific and machine-learning stack: Numpy, Torch/Tensorflow/Jax, Scikit-learn, Polars, SQL.
Hyper Recruitment Solutions Ltd (HRS) is an Equal Opportunities employer. We welcome applications from anyone who meets the role requirements. HRS exclusively supports the Life Science sectors, combining recruitment expertise with scientific knowledge to help you advance your career.
Machine Learning Engineer in London employer: Hyper Recruitment Solutions
As a leading research organisation based in London, we pride ourselves on fostering a collaborative and innovative work culture that empowers our employees to push the boundaries of machine learning and biophysics. Our commitment to employee growth is evident through continuous learning opportunities and mentorship from industry experts, making this an ideal environment for those seeking meaningful and impactful work in cutting-edge research. Join us to be part of a team that values creativity, diversity, and the pursuit of scientific excellence.
Contact Detail:
Hyper Recruitment Solutions Recruiting Team
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer in London
✨Tip Number 1
Network like a pro! Reach out to professionals in the machine learning field on LinkedIn or at industry events. We can’t stress enough how valuable personal connections can be in landing that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to NMR data and deep learning. We recommend sharing this on platforms like GitHub to 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. We suggest practicing common machine learning scenarios and being ready to discuss your past experiences in detail.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities waiting for talented Machine Learning Engineers like you. It’s a great way to get noticed and show your enthusiasm for the role.
We think you need these skills to ace Machine Learning Engineer in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your relevant experience with machine learning, model-building, and any specific projects related to NMR data or drug discovery. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about this role and how your background makes you a perfect fit. Don’t forget to mention any innovative approaches you've taken in your previous work that relate to our key duties.
Showcase Your Technical Skills:Be sure to list your technical skills prominently, especially your experience with Python and the scientific stack we mentioned. If you've worked with tools like TensorFlow or Scikit-learn, let us know! We love seeing practical examples of your expertise.
Apply Through Our Website:We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates. Plus, it’s super easy!
How to prepare for a job interview at Hyper Recruitment Solutions
✨Know Your Tech Stack
Make sure you’re well-versed in the specific tools and technologies mentioned in the job description, like Python, Numpy, and TensorFlow. Brush up on your knowledge of these frameworks and be ready to discuss how you've used them in past projects.
✨Showcase Your Problem-Solving Skills
Prepare to talk about specific challenges you've faced in machine learning projects, especially those involving graph-based data or disordered proteins. Use the STAR method (Situation, Task, Action, Result) to structure your answers and highlight your innovative approaches.
✨Collaborative Mindset
Since collaboration is key in this role, think of examples where you’ve worked closely with interdisciplinary teams. Be ready to discuss how you’ve mentored others or contributed to team projects, particularly in computational biophysics or data analysis.
✨Stay Updated on Trends
Demonstrate your passion for the field by discussing recent breakthroughs in machine learning and NMR. This shows that you’re not just knowledgeable but also genuinely interested in advancing the research landscape.
