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- Tasks: Shape the future of AI by developing cutting-edge models and optimising training infrastructure.
- Company: Join a fast-growing AI startup with ambitious research goals.
- Benefits: High-impact role with ownership, competitive salary, and growth opportunities.
- Other info: Collaborate with top researchers and contribute to leading AI publications.
- Why this job: Make a real difference in AI while working on innovative projects from day one.
- Qualifications: PhD or Master’s in Machine Learning or related field; experience in ML systems required.
The predicted salary is between 60000 - 80000 £ per year.
We’re partnering with an early-stage AI startup building next-generation foundation models that significantly improve the performance of LLMs when working with structured data. The company is doubling in size this year, with ambitious research goals and exciting growth opportunities. This is a high-ownership, high-impact role where you will help shape the research direction from day one.
As a Research Engineer, you will work closely with researchers to develop state-of-the-art models and bring them into production.
- Own the model pre-training stack and optimise training infrastructure
- Write low-level GPU kernels to address performance bottlenecks
- Contribute to publications at leading international AI conferences
Requirements:
- PhD or Master’s degree in Machine Learning, Computer Science, or a related field
- Proven experience in ML systems, with a focus on training and inference optimisation
- Experience writing CUDA kernels
- Exceptional proficiency in Python and PyTorch
- Experience with distributed training frameworks
- Experience working with structured data (tabular/time-series) is a plus
Experienced Research Engineer employer: Vertex Search
Contact Detail:
Vertex Search Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Experienced Research Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and machine learning community, especially those who are already working at startups. Attend meetups, webinars, or conferences where you can connect with potential colleagues and learn about job openings before they even hit the market.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving GPU kernels or distributed training frameworks. This will give you an edge when discussing your experience during interviews and demonstrate your hands-on expertise.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and PyTorch skills. Practice coding challenges that focus on ML systems and optimisation techniques. The more comfortable you are with these tools, the better you'll perform when it counts!
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Tailor your application to highlight your relevant experience and passion for AI research, and let us know how you can contribute to our ambitious goals.
We think you need these skills to ace Experienced Research Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the role of Research Engineer. Highlight your expertise in ML systems, CUDA kernels, and any relevant research publications to catch our eye!
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about AI and how your background makes you a perfect fit for this high-impact role. Share specific examples of your work with structured data and model optimisation.
Showcase Your Projects: If you've worked on any interesting projects or research, don’t hesitate to include them! We love seeing practical applications of your skills, especially if they relate to training and inference optimisation.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity in our growing team!
How to prepare for a job interview at Vertex Search
✨Know Your Stuff
Make sure you brush up on your knowledge of machine learning systems, especially around training and inference optimisation. Be ready to discuss your experience with CUDA kernels and how you've tackled performance bottlenecks in the past.
✨Showcase Your Projects
Prepare to talk about specific projects where you've developed state-of-the-art models or optimised training infrastructure. Highlight any publications you've contributed to, even if they’re not from top conferences, as this shows your engagement with the research community.
✨Get Technical
Since this role involves writing low-level GPU kernels, be prepared for technical questions or even a coding challenge. Brush up on your Python and PyTorch skills, and think through how you would approach distributed training frameworks.
✨Ask Insightful Questions
Demonstrate your interest in the company’s research goals by asking thoughtful questions about their current projects and future directions. This shows that you’re not just looking for any job, but are genuinely excited about contributing to their mission.