At a Glance
- Tasks: Own and build critical infrastructure for AI search systems at scale.
- Company: Join a pioneering team revolutionising AI search technology.
- Benefits: Competitive salary, flexible work environment, and opportunities for professional growth.
- Other info: Be part of a small, dynamic team where your contributions directly influence the product.
- Why this job: Shape the future of AI by building foundational systems that impact millions.
- Qualifications: 3+ years in systems engineering with expertise in distributed systems and cloud infrastructure.
The predicted salary is between 60000 - 80000 € per year.
About Us and the Problem
We're building the best search for AI. Search is becoming the core primitive for AI. Every agent workflow, every reasoning system, every tool call needs context from the web and beyond, but today's search infrastructure was built for a different era. We're building the stack that makes the web accessible to how agents actually work: content at scale, structured for reasoning, designed for deep research across thousands of sources. Information Retrieval is in a renaissance, LLMs are changing not just what we need to build but how we can build it, and we combine first-principles research with hard engineering to solve web-scale indexing for agentic use. How agents access the web is one of the foundational questions of the next decade of software, and the infrastructure is what makes the answer fast, cheap, and possible at scale. You'd be building the layer underneath everything: storage, retrieval, indexing, serving. The kind of work that ends up in papers and the kind of work that ends up running in production, often the same week.
What You'll Be Doing
- Own critical infrastructure end-to-end: Take a layer of the stack (search and storage, data lake, ingestion, serving, or ML systems) from architecture through production.
- Set the technical direction: Make the hard calls on storage formats, query algorithms, scheduling models, and cost vs latency trade-offs that the team will live with for years.
- Engineer at petabyte scale: Drive concurrency, networking, IO, caching, scheduling, query latency, and cost down to the last percent.
Who You Are
A mid to senior-level engineer with 3+ years building, shipping, and operating systems-intensive infrastructure in production. Deep experience in distributed systems, operating systems, networking, databases, data engineering, search infrastructure, or ML systems with the judgement and taste that comes from having owned them at scale. Our systems are written in Rust and Python. You must be fluent in these and other systems languages (C, C++, Go) are a bonus. Comfortable working with cloud infrastructure, containerised workloads, Kubernetes, and large compute environments. Have made architectural decisions you've had to live with, and know what you'd do differently next time. Strong builder who wants meaningful ownership over real infrastructure and is comfortable using AI tools as part of their engineering workflow.
Bonus Points
- Prior experience scaling infrastructure at a search, large-scale data, or ML company.
- Track record of owning production systems at scale.
- Have built or significantly contributed to open-source infrastructure projects (databases, distributed systems, data engines).
- Published research, conference talks, or writing on systems, IR, or ML infrastructure.
- Experience with GCP, AWS, Kubernetes, Terraform, observability, workload orchestration, or large distributed worker pools.
- Experience with search databases, vector databases, retrieval systems, feature stores, training pipelines, or evaluation infrastructure.
Why Join
We are a small team with few abstractions between you and the system you're building. The infrastructure is the product, what you ship is the infrastructure agents run on. As a senior hire, you'll shape the direction of a foundational layer, not just contribute to it. If you want to build systems that will serve as core primitives for agents, reach out.
Systems & Infrastructure Engineer in London employer: Valyu
Join a pioneering team dedicated to revolutionising AI search infrastructure, where your contributions directly impact the future of information retrieval. We foster a collaborative work culture that values innovation and ownership, offering ample opportunities for professional growth and the chance to work with cutting-edge technologies in a dynamic environment. Located in a vibrant tech hub, we provide a unique opportunity to engage with like-minded professionals while shaping the foundational layers of AI systems.
StudySmarter Expert Advice🤫
We think this is how you could land Systems & Infrastructure Engineer in London
✨Tip Number 1
Network like a pro! Attend industry meetups, tech conferences, or even local coding events. Chatting with folks in the field can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Build a personal project or contribute to open-source. This not only sharpens your abilities but also gives you something tangible to discuss during interviews.
✨Tip Number 3
Prepare for technical interviews by practicing coding challenges and system design problems. Use platforms like LeetCode or HackerRank to get into the groove of solving real-world problems.
✨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, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Systems & Infrastructure Engineer in London
Some tips for your application 🫡
Show Your Passion for Infrastructure:When writing your application, let us see your enthusiasm for systems and infrastructure. Share specific examples of projects you've worked on that relate to the role, especially those involving distributed systems or search infrastructure.
Be Clear and Concise:We appreciate clarity! Make sure your application is easy to read and straight to the point. Highlight your key experiences and skills without unnecessary fluff, so we can quickly see how you fit into our team.
Tailor Your Application:Don’t just send a generic application. Tailor it to our job description! Mention relevant technologies like Rust, Python, or any cloud infrastructure experience you have. Show us that you understand what we’re building and how you can contribute.
Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It helps us keep track of applications and ensures you’re considered for the role. Plus, it’s super easy!
How to prepare for a job interview at Valyu
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, especially Rust and Python. Brush up on your knowledge of distributed systems, cloud infrastructure, and container orchestration with Kubernetes. Being able to discuss your experience with these tools will show that you're ready to hit the ground running.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced in previous roles, particularly around scaling infrastructure or making architectural decisions. Use the STAR method (Situation, Task, Action, Result) to structure your answers, highlighting how your decisions impacted the systems you worked on.
✨Understand the Company’s Vision
Familiarise yourself with the company’s mission to revolutionise search for AI. Be ready to discuss how your skills can contribute to building the infrastructure that supports this vision. Showing that you understand their goals will demonstrate your genuine interest in the role.
✨Ask Insightful Questions
Prepare thoughtful questions about the team’s current projects, challenges they face, and their approach to engineering at scale. This not only shows your enthusiasm but also helps you gauge if the company culture and work align with your career aspirations.
