At a Glance
- Tasks: Lead the deployment of ML systems for massive connectomics data and automate image analysis.
- Company: Join the UKRI's MRC Laboratory of Molecular Biology, a leader in scientific research.
- Benefits: Competitive salary, cutting-edge resources, and opportunities for professional growth.
- Other info: Work in a dynamic environment with access to advanced technology and research.
- Why this job: Make a real impact in science by transforming ML models into production-ready solutions.
- Qualifications: Experience in ML systems, strong programming skills, and a passion for collaboration.
The predicted salary is between 60000 - 84000 £ per year.
UKRI's MRC Laboratory of Molecular Biology seeks a scientist to lead deployment and scaling of ML systems for petabyte-scale connectomics data. The role focuses on turning ML models into robust, observable production pipelines for automated segmentation and large-scale image analysis.
You will design and maintain infrastructure for training, inference, monitoring and data processing across GPU/CPU clusters, on-prem storage and HPC environments, collaborating with scientists and software.
Production ML Engineer for Petabyte-Scale Connectomics in Cambridge employer: UKRI
The MRC Laboratory of Molecular Biology (LMB) is an exceptional employer, offering a dynamic work environment that fosters professional growth and collaboration. With a strong commitment to employee development, the LMB provides comprehensive training opportunities and encourages continuous learning, making it an ideal place for HR professionals looking to make a meaningful impact in the scientific community. Located in the vibrant Cambridge Biomedical Campus, employees benefit from a supportive culture that values innovation and teamwork, ensuring a rewarding career path in Human Resources.
StudySmarter Expert Advice🤫
We think this is how you could land Production ML Engineer for Petabyte-Scale Connectomics in Cambridge
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We think you need these skills to ace Production ML Engineer for Petabyte-Scale Connectomics in Cambridge
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Craft a Tailored Cover Letter:For a full-time role at UKRI, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at UKRI. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at UKRI
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
