At a Glance
- Tasks: Design and optimise high-performance AI inference systems for massive workloads.
- Company: Join a high-growth AI infrastructure startup with a world-class engineering team.
- Benefits: Competitive salary in the 75th-90th percentile plus equity opportunities.
- Other info: Fast-paced environment with significant career growth potential.
- Why this job: Be part of building one of Europe's premier AI inference teams from scratch.
- Qualifications: Experience in systems programming and high-performance computing is essential.
The predicted salary is between 80000 - 100000 £ per year.
You will join a world-class engineering team building a high-performance inference server designed for scale. This role focuses on optimizing batched inference workflows and low-level systems architecture to support massive AI workloads. You will play a pivotal role in establishing one of Europe’s premier inference teams while working on cutting-edge hardware-software integration.
Location: London, UK
Why this role is remarkable:
- Opportunity to build one of the strongest AI inference teams in Europe from the ground up.
- Backed by top-tier VCs with a focus on high-performance computing and infrastructure.
- Highly competitive compensation package featuring 75th-90th percentile salary and significant equity upside.
What You Will Do:
- Design and implement high-performance batched inference systems for large-scale AI models.
- Optimize low-level system performance and hardware utilization to reduce latency and increase throughput.
- Collaborate with cross-functional teams to architect scalable infrastructure for next-generation AI applications.
The Ideal Candidate:
- Extensive experience in systems programming, low-level optimization, or high-performance computing (HPC).
- Proven track record of working on inference engines, compilers, or distributed systems at scale.
- Strong architectural intuition and the ability to thrive in a fast-paced, in-person engineering culture.
Member of Technical Staff (75th–90th percentile salary + Equity) at high-growth AI infrastructu[...] employer: Jack & Jill
Join a high-growth AI infrastructure startup in London, where you will be part of a world-class engineering team dedicated to building cutting-edge batched inference systems. With a focus on employee growth and innovation, the company offers a highly competitive compensation package, including equity, and fosters a collaborative work culture that encourages creativity and technical excellence. This is an exceptional opportunity to shape one of Europe's premier AI inference teams while working with top-tier VCs in a dynamic environment.
StudySmarter Expert Advice🤫
We think this is how you could land Member of Technical Staff (75th–90th percentile salary + Equity) at high-growth AI infrastructu[...]
✨Tip Number 1
Network like a pro! Reach out to current employees on LinkedIn or at industry events. A friendly chat can give you insider info and maybe even a referral, which can really boost your chances.
✨Tip Number 2
Prepare for the interview by diving deep into the company’s tech stack and recent projects. Show us you’re not just interested in the role but also passionate about what we do and how you can contribute.
✨Tip Number 3
Practice your coding skills and system design concepts. We love seeing candidates who can think on their feet and tackle real-world problems, so brush up on those technical skills before the big day!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows us you’re genuinely interested in joining our team.
We think you need these skills to ace Member of Technical Staff (75th–90th percentile salary + Equity) at high-growth AI infrastructu[...]
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the role of Member of Technical Staff. Highlight your experience in systems programming and low-level optimisation, as these are key for us. Use specific examples that showcase your skills in high-performance computing.
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Tell us why you’re excited about building a high-performance inference server and how your background aligns with our mission. Keep it concise but impactful, showing your passion for AI infrastructure.
Showcase Relevant Projects:If you've worked on any projects related to inference engines or distributed systems, make sure to include them in your application. We love seeing real-world applications of your skills, so don’t hold back on the details!
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. Plus, it shows us you’re keen to join our team!
How to prepare for a job interview at Jack & Jill
✨Know Your Tech Inside Out
Make sure you brush up on your knowledge of systems programming and low-level optimisation. Be ready to discuss specific projects you've worked on, especially those involving inference engines or distributed systems. This will show that you have the hands-on experience they’re looking for.
✨Showcase Your Problem-Solving Skills
Prepare to tackle some technical challenges during the interview. Think about how you would optimise batched inference workflows or improve system performance. Practising common coding problems can help you articulate your thought process clearly.
✨Understand Their Vision
Research the company’s goals and their approach to AI infrastructure. Being able to discuss how your skills align with their mission will demonstrate your genuine interest in the role and the company. It’s all about showing that you’re not just a fit for the job, but also for their culture.
✨Ask Insightful Questions
Prepare thoughtful questions that reflect your understanding of high-performance computing and the challenges they face. This could be about their current projects or future plans for scaling their inference server. Engaging in this way shows that you’re proactive and invested in the role.
