At a Glance
- Tasks: Lead the ML Infrastructure team and automate release processes for cutting-edge AI systems.
- Company: Pavo, a pioneering company in Enterprise Superintelligence with a focus on innovative solutions.
- Benefits: Founding equity, technical challenges, and collaboration with top-tier talent.
- Other info: Inclusive environment celebrating diversity and offering significant career growth.
- Why this job: Join a world-class team and shape the future of AI infrastructure.
- Qualifications: Experience in MLOps, DevOps, and cloud platforms; strong engineering and communication skills.
The predicted salary is between 63000 - 77000 £ per year.
About Pavo
Pavo is building Enterprise Superintelligence: compounding systems that take ownership of business outcomes and work with humans to deliver them.
We believe that while foundation models are necessary, they are not sufficient.
The hard problem is systems intelligence: end-to-end architectures that understand a company's code, data, and decisions, and improve themselves through experience.
We are assembling a small, senior team of researchers and engineers obsessed with systems-first intelligence.
Our current team consists of Ph Ds and ML engineers from top applied ML and coding agent companies, with a heritage of shipping systems at Spotify, Share Chat, and Sourcegraph scale.
Our team has built impressive momentum with a small group of highly capable engineers and researchers.
The Opportunity
As a Founding Systems Engineer, you will lead our ML Infrastructure team, driving Dev Ops, MLOps, and Agent Ops across both R&D and production environments.
You will own the automation of release and evaluation processes and collaborate closely with cross-functional teams to support their projects.
This is a critical role for a builder who thrives at the intersection of ML research infrastructure and production systems, and wants to define the engineering culture of a fast-paced AI company from the ground up.
- What You'll Build
- ML Infrastructure Leadership: Lead the ML Infrastructure team working on Dev Ops, MLOps, and Agent Ops for both R&D and production environments.
- Release & Evaluation Automation: Automate the release and evaluation processes for research and production, ensuring reliable and efficient delivery of ML systems.
- Cross-Functional Collaboration: Collaborate with cross-functional teams and support their projects, acting as a key enabler across the organisation.
- What We Are Looking For
- MLOps & Dev
Ops Tooling: Proven track record of working with systems such as Argo CD, Kargo, Jenkins, Vertex AI, Sage Maker, and similar platforms.
- ML Infrastructure Experience: Proven track record of working on ML research and production infrastructure.
- Infrastructure as Code: Knowledge of Ia C tools such as Terraform and Helm.
- Cloud Expertise: Excellent familiarity with at least one of the hyperscaler clouds (GCP, AWS, Azure), and familiarity with the others.
- Engineering Excellence: Excellent software engineering and problem-solving skills.
- Communication: Excellent interpersonal and communication skills.
- Why Join Us
- Founding Equity: Significant ownership in a company tackling the next layer of the AI stack.
- Technical Challenge: Solve novel infrastructure problems related to secure agentic execution and "orgs in a box."
- World-Class Team: Collaborate with a dense talent cluster of researchers and engineers who have shipped products serving hundreds of millions of users.
Pavo is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
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Founding Systems Engineer (Infrastructure) employer: Pavo AI
Pavo is an exceptional employer for those looking to make a significant impact in the AI landscape. With a focus on building Enterprise Superintelligence, employees enjoy the opportunity to work alongside a world-class team of experts while having a stake in the company's success through founding equity. The collaborative and inclusive work culture fosters innovation and personal growth, making it an ideal environment for engineers eager to tackle complex challenges in ML infrastructure.
StudySmarter Expert Advice🤫
We think this is how you could land Founding Systems Engineer (Infrastructure)
✨Join Local Tech Meetups
Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at Pavo AI or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!
✨Contribute to Open Source Projects
Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to Pavo AI.
✨Tap into Online Developer Communities
Don’t underestimate the power of online developer communities like GitHub, Stack Overflow, and even Reddit. Participate in discussions, share your projects, and build your visibility. We can often find opportunities through these channels that can lead to a full-time gig at companies like Pavo AI.
✨Explore Job Boards Specifically for Tech Roles
Keep your eyes peeled on job boards that focus on tech roles. Sites like TechCareers or Stack Overflow Jobs can often have listings for companies like Pavo AI that might not show up on broader job sites. Make it a habit to check these regularly, and don’t hesitate to apply directly through our website!
We think you need these skills to ace Founding Systems Engineer (Infrastructure)
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at Pavo AI.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Pavo AI and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at Pavo AI
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If Pavo AI uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.
