At a Glance
- Tasks: Architect and optimise high-throughput AI systems for large-scale generative models.
- Company: Well-funded AI infrastructure startup with a focus on innovation.
- Benefits: Competitive salary, dynamic work environment, and opportunities for rapid career growth.
- Other info: Collaborate with top-tier researchers in a fast-paced, impactful setting.
- Why this job: Join an elite team shaping the future of AI deployment and scalability.
- Qualifications: Expertise in systems programming, C++ or Rust, and deep learning frameworks.
The predicted salary is between 60000 - 80000 £ per year.
As a core member of the technical staff, you will architect and optimize high-throughput inference systems for large-scale generative models. You will tackle deep technical challenges in distributed systems and hardware-software co-design, directly impacting the latency and scalability of production-grade AI services for a global developer ecosystem.
Location: London, UK
Why this role is remarkable:
- Work at the intersection of systems engineering and cutting‑edge machine learning research to define the future of model deployment.
- Join an elite technical team backed by top‑tier venture capital firms during a period of rapid infrastructure scaling.
- Influence the foundational layer of AI applications by building systems that make massive models commercially viable and performant.
What You Will Do:
- Design and implement low‑level optimizations for model inference to maximize GPU utilization and minimize token latency.
- Build robust, distributed systems capable of serving frontier models with high reliability and cost‑efficiency.
- Collaborate with research teams to integrate novel architectures into production‑ready inference engines and serving stacks.
The ideal candidate:
- Demonstrates deep expertise in systems programming and optimizing performance‑critical software in C++ or Rust.
- Has a proven track record of working with deep learning frameworks and low‑level GPU acceleration libraries.
- Possesses a strong understanding of distributed systems and the mechanics of modern large language model architectures.
Member of Technical Staff: AI Systems Engineer at well-funded AI infrastructure startup employer: Jack & Jill
Join a well-funded AI infrastructure startup in London, where you will be part of an elite technical team dedicated to shaping the future of AI model deployment. Enjoy a dynamic work culture that fosters innovation and collaboration, with ample opportunities for professional growth as you tackle complex challenges in systems engineering and machine learning. Benefit from being at the forefront of technology, backed by top-tier venture capital, while contributing to impactful projects that enhance the performance and scalability of AI services globally.
StudySmarter Expert Advice🤫
We think this is how you could land Member of Technical Staff: AI Systems Engineer at well-funded AI infrastructure startup
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and tech community, especially those already working at startups. Attend meetups, webinars, or even online forums to get your name out there and learn about potential openings.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects related to AI systems and optimisations. This gives you a chance to demonstrate your expertise in C++ or Rust and your understanding of distributed systems.
✨Tip Number 3
Prepare for technical interviews by brushing up on system design and optimisation techniques. Practice coding challenges that focus on performance-critical software, as this will help you stand out during the interview process.
✨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 you’re genuinely interested in joining our team and contributing to cutting-edge AI infrastructure.
We think you need these skills to ace Member of Technical Staff: AI Systems Engineer at well-funded AI infrastructure startup
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the role of AI Systems Engineer. Highlight your experience with systems programming, C++ or Rust, and any work you've done with deep learning frameworks. We want to see how your skills align with our needs!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about AI infrastructure and how you can contribute to our team. Be specific about your past experiences and how they relate to the challenges we face.
Showcase Your Projects:If you've worked on relevant projects, make sure to include them in your application. Whether it's optimising inference systems or building distributed architectures, we love seeing practical examples of your work that demonstrate your expertise.
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 the role. Plus, it shows you’re keen on joining our team at this exciting time!
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, especially in C++ or Rust. Be ready to discuss specific projects where you've optimised performance-critical software, as this will show your hands-on experience and technical depth.
✨Understand the Role's Impact
Familiarise yourself with how high-throughput inference systems work and their importance in AI applications. Be prepared to explain how your contributions can enhance GPU utilisation and reduce latency, demonstrating that you understand the role's significance in the broader context of AI infrastructure.
✨Showcase Collaboration Skills
Since you'll be working closely with research teams, think of examples where you've successfully collaborated on projects. Highlight your ability to integrate novel architectures into production-ready systems, as this will illustrate your teamwork and adaptability in a fast-paced environment.
✨Prepare for Technical Challenges
Expect to tackle some deep technical questions during the interview. Practice explaining complex concepts related to distributed systems and large language model architectures clearly and concisely. This will help you convey your expertise effectively and demonstrate your problem-solving skills.
