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- Tasks: Join us as a Founding ML Engineer to design and maintain cutting-edge ML infrastructure.
- Company: Be part of an innovative startup shaping the future of machine learning.
- Benefits: Enjoy flexible work options and a dynamic, collaborative environment.
- Why this job: Make a real impact in a fast-paced setting while working with advanced technologies.
- Qualifications: 3+ years in backend engineering, proficient in Python, and experienced with cloud platforms.
- Other info: Ideal for those who thrive in ambiguity and want to take ownership of their projects.
The predicted salary is between 43200 - 72000 £ per year.
3+ years of experience in backend engineering and applied machine learning.
Ability to design, build, and maintain scalable backend and ML infrastructure.
Proficient in Python.
Experience deploying services and machine learning models to cloud platforms such as Google Cloud, Azure, etc.
Hands-on experience with Large Language Models (LLMs) and modern ML architectures.
(Bonus) Familiarity with knowledge graphs and their integration into ML systems.
Comfortable working in fast-paced, ambiguous startup environments.
Strong ownership mentality and ability to work across the stack when needed.
Contact Detail:
Pinnacle Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Founding ML Engineer
✨Tip Number 1
Make sure to showcase your experience with backend engineering and machine learning in your conversations. Be ready to discuss specific projects where you've designed and built scalable systems, as this will demonstrate your hands-on expertise.
✨Tip Number 2
Familiarise yourself with the cloud platforms mentioned in the job description, like Google Cloud and Azure. If you have any personal projects or contributions that involved deploying ML models on these platforms, be prepared to share those experiences.
✨Tip Number 3
Since familiarity with Large Language Models (LLMs) is crucial, brush up on recent advancements in this area. Being able to discuss current trends and your practical experience with LLMs will set you apart from other candidates.
✨Tip Number 4
Emphasise your adaptability and ownership mentality during discussions. Startups thrive on individuals who can navigate ambiguity and take initiative, so share examples of how you've successfully worked across different areas in previous roles.
We think you need these skills to ace Founding ML Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your 3+ years of experience in backend engineering and applied machine learning. Emphasise your proficiency in Python and any relevant projects that showcase your ability to design and maintain scalable backend and ML infrastructure.
Showcase Relevant Experience: In your cover letter, detail your experience with deploying services and machine learning models to cloud platforms like Google Cloud or Azure. Mention specific projects where you have worked with Large Language Models (LLMs) and modern ML architectures.
Demonstrate Problem-Solving Skills: Highlight instances where you've thrived in fast-paced, ambiguous environments. Use examples that illustrate your strong ownership mentality and ability to work across the stack when needed.
Express Enthusiasm for Innovation: Convey your passion for working in a startup environment and your eagerness to contribute to innovative projects. If you have familiarity with knowledge graphs, mention how this could enhance the ML systems you would be working on.
How to prepare for a job interview at Pinnacle
✨Showcase Your Technical Skills
Be prepared to discuss your experience with backend engineering and machine learning. Highlight specific projects where you've designed, built, or maintained scalable infrastructure, especially using Python.
✨Demonstrate Cloud Proficiency
Since deploying services and ML models to cloud platforms is crucial, be ready to talk about your experience with Google Cloud, Azure, or similar platforms. Share examples of how you've successfully managed deployments in the past.
✨Discuss Large Language Models
If you have hands-on experience with Large Language Models (LLMs), make sure to bring it up. Discuss any relevant projects or challenges you've faced while working with modern ML architectures.
✨Emphasise Adaptability and Ownership
In a fast-paced startup environment, adaptability is key. Share instances where you've taken ownership of projects and worked across different areas of the stack, showcasing your ability to thrive in ambiguity.
