At a Glance
- Tasks: Develop and benchmark innovative machine learning algorithms and applications.
- Company: Join a pioneering deep-tech organisation at the forefront of AI and computing.
- Benefits: Competitive salary, collaborative environment, and exposure to cutting-edge technology.
- Other info: Work alongside top researchers and engineers in a dynamic, innovative setting.
- Why this job: Make a real impact by solving complex challenges with advanced AI solutions.
- Qualifications: Experience in machine learning, strong Python skills, and a relevant MSc or PhD.
The predicted salary is between 60000 - 80000 £ per year.
We're partnering with an innovative deep-tech organisation developing next-generation computing technology and advanced AI solutions.
As the company continues to expand its research capabilities, they're looking to appoint a Machine Learning Research Engineer to help drive the development of novel machine learning algorithms and applications.
This is a rare opportunity to work on cutting-edge technology at the intersection of machine learning, advanced computing, and emerging hardware platforms, helping to solve complex real-world challenges for customers and partners worldwide.
The Opportunity
As a Machine Learning Research Engineer, you'll develop and benchmark new machine learning approaches, with a particular focus on generative AI and advanced neural network architectures.
You'll work alongside a multidisciplinary team of researchers and engineers, translating innovative ideas into practical solutions and customer-facing applications.
Key Responsibilities
- Develop, test and benchmark advanced machine learning algorithms.
- Work with generative AI models including diffusion models, flow models and GANs.
- Design and evaluate novel hybrid machine learning architectures.
- Collaborate with customers and strategic partners to identify and solve complex technical challenges.
- Contribute to software products through the development of new algorithms, tools and examples.
- Support research activities through experimentation, analysis and publication of results.
- Contribute to the creation and protection of intellectual property arising from your work.
Qualifications
- Proven experience developing and benchmarking machine learning algorithms.
- Hands‑on experience with diffusion models, flow models and/or GANs.
- Strong programming skills in Python and Py Torch.
- Experience using version control systems such as Git.
- MSc or Ph D in Computer Science, Machine Learning, Physics, Mathematics or a related discipline.
- Excellent communication skills and the ability to work effectively within multidisciplinary teams.
- Experience working with advanced computing infrastructure including HPC, GPUs, NPUs, ASICs or other specialist hardware.
- Experience training and optimising multi‑GPU models.
- Published research in machine learning or a related field.
- Experience working directly with customers or external stakeholders.
- An interest in or understanding of quantum computing technologies.
- What's On Offer
- The opportunity to work on genuinely innovative technology at the forefront of AI and advanced computing.
- A collaborative environment alongside highly experienced researchers and engineers.
- Exposure to cutting‑edge machine learning research and commercial applications.
- Competitive salary and benefits package.
- #J-18808-Ljbffr
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Research Engineer
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like DeepRec.ai.
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We think you need these skills to ace Machine Learning Research Engineer
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!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at DeepRec.ai, 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 DeepRec.ai. 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 DeepRec.ai
✨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
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at DeepRec.ai!
✨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.
