How your CV stacks up
Upload your CV to see how well it fits this job role
?%
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 PhDs and ML engineers from top applied ML and coding agent companies, with a heritage of shipping systems at Spotify, ShareChat, and Sourcegraph scale.
Our team has built impressive momentum with a small group of highly capable engineers and researchers.
The Opportunity
As a Founding ML Engineer focused on Agentic Systems, you will contribute to build the foundational technologies for enterprise superintelligence: you help architect memory, reasoning, and learning capabilities of our autonomous agents.
You will tackle hard problems in long-horizon planning, knowledge representation, and agentic learning, combining state-of-the-art research with world-class engineering to build a new computing paradigm. If you are a world expert in search, retrieval, or reinforcement learning who is driven to define the future of agentic AI, this is your ideal role.
What You'll Do
You will define the capabilities of our AI teammates by working on:
- Build Foundational Agent Memory: Design and scale the foundational long-horizon memory and knowledge infrastructure that will allow agents to learn, recall, and reason over vast, evolving enterprise contexts. This is not about simple RAG; it's about pioneering the future of agent memory (episodic, semantic, and procedural).
- Innovate in Search & Retrieval: Develop state-of-the-art search and retriever systems involving multi-stage retrieval, novel indexing techniques, and new forms of knowledge representation to ensure agents find the exact needle in the enterprise haystack.
- Define Agentic Learning: Apply deep expertise in reinforcement learning and reasoning to guide, shape, and advance agentic behavior, enabling them to tackle complex, multi-step tasks reliably.
- Create Synthetic Worlds: Contribute to the creation of synthetic world models and complex simulation environments where agents can train, experiment, and learn at a massive scale before deploying to production.
- Architect for Scale: Design, build, and deploy robust, enterprise-grade systems built for a future of trillion-agent interactions, solving for both cloud and on-prem deployments.
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
What We Are Looking For
We are looking for builders who are operating at the edge of what's possible in AI.
Core Qualifications
- Experience: 5+ years of industry or academic experience as a Machine Learning Engineer, Applied Scientist, or Research Scientist.
- Specialized Expertise: World-class expertise in one of the following areas:
- Search & Retrieval: Deep experience designing and scaling state-of-the-art systems (e.g., multi-stage retrieval, dense retrieval, novel indexing).
- Reinforcement Learning & Reasoning: Deep expertise in applying RL to complex tasks, agent-based modeling, or automated planning.
- Engineering Excellence: Strong software engineering fundamentals and experience building robust, scalable systems in a production environment. You bridge the gap between "research code" and "production infrastructure."
- Research to Practice: Proven ability to translate cutting-edge research into practical, high-impact applications.
Get help with your application
Your very own career expert that helps elevate your application to the next level.
Preferred Qualifications
- A strong track record of publications in top-tier ML conferences (e.g., NeurIPS, ICML, ICLR, SIGIR, KDD, WebConf).
- Experience building and designing large-scale infrastructure for a "new computing paradigm" (e.g., agent frameworks, vector databases, massive simulation engines).
- Experience building or working with simulation environments and synthetic data generation.
- M.S. or Ph.D. in Computer Science, Machine Learning, or a related quantitative field, or equivalent exceptional experience.
Why Join Us
- Founding Equity: Significant ownership in a company tackling the next layer of the AI stack.
- Hard Problems: Work on unsolved problems in agentic reasoning, memory, and reinforcement learning.
- 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.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Skills
Location