At a Glance
- Tasks: Optimise ML inference for edge devices and GPUs, ensuring efficient operation of large models.
- Company: Icehouseventures, a forward-thinking company focused on innovation and inclusion.
- Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
- Other info: Collaborative environment that encourages creativity and innovation.
- Why this job: Join a dynamic team and make a real impact in the world of machine learning.
- Qualifications: Experience in ML performance engineering and a passion for cutting-edge technology.
The predicted salary is between 60000 - 80000 £ per year.
Icehouseventures is looking for a Staff ML Performance Engineer to optimize ML inference for edge accelerators and GPUs. You will focus on running large transformer-based models efficiently on low-cost, low-power devices, directing technical projects and ensuring reliable operation in vehicular compute environments.
This full-time role operates under a hybrid policy, balancing office collaboration with remote work. Candidates can expect a dynamic environment, fostering innovation and inclusion.
Staff ML Performance Engineer — Edge Inference Optimizer employer: Icehouseventures
Icehouseventures is an exceptional employer, offering a dynamic work environment that fosters innovation and inclusion for its Staff ML Performance Engineer role. With a hybrid work policy, employees enjoy the flexibility of remote work while collaborating in the office, alongside ample opportunities for professional growth and development in cutting-edge technology focused on optimizing ML inference for edge devices.
StudySmarter Expert Advice🤫
We think this is how you could land Staff ML Performance Engineer — Edge Inference Optimizer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those working with edge inference and ML performance. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! If you’ve got projects or contributions related to transformer models or edge devices, make sure to highlight them in conversations. We love seeing real-world applications of your expertise.
✨Tip Number 3
Prepare for technical interviews by brushing up on your knowledge of ML optimisations and vehicular compute environments. We want to see how you think on your feet, so practice explaining your thought process clearly.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets the attention it deserves. Plus, we love seeing candidates who take that extra step.
We think you need these skills to ace Staff ML Performance Engineer — Edge Inference Optimizer
Some tips for your application 🫡
Show Your Passion for ML:When writing your application, let us see your enthusiasm for machine learning! Share any personal projects or experiences that highlight your skills in optimising ML inference, especially on edge devices.
Tailor Your CV:Make sure your CV is tailored to the role. Highlight relevant experience with transformer-based models and any work you've done with low-cost, low-power devices. We want to see how your background aligns with our needs!
Be Clear and Concise:Keep your application clear and to the point. Use bullet points where possible to make it easy for us to read through your qualifications and achievements. We appreciate a well-structured application!
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team at StudySmarter!
How to prepare for a job interview at Icehouseventures
✨Know Your Tech Inside Out
Make sure you’re well-versed in the latest ML inference techniques, especially for edge devices. Brush up on transformer models and how they perform on low-cost hardware. Being able to discuss specific projects or experiences where you've optimised performance will really impress.
✨Showcase Your Problem-Solving Skills
Prepare to discuss challenges you've faced in previous roles, particularly in vehicular compute environments. Think of examples where you directed technical projects and how you ensured reliable operation. This will demonstrate your ability to tackle real-world issues.
✨Emphasise Collaboration and Innovation
Since this role involves a hybrid work model, highlight your experience working in dynamic teams. Share examples of how you’ve fostered innovation and inclusion in past projects. Companies love candidates who can thrive in collaborative settings!
✨Ask Insightful Questions
Prepare thoughtful questions about the company’s approach to ML performance and their future projects. This shows your genuine interest in the role and helps you gauge if the company culture aligns with your values.
