Founding Applied Scientist
Build AI systems that learn from experience to move real business metrics
London / Remote Full-time Pavo Labs
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 Applied Scientist, you will operate at the intersection of research and engineering, building the core systems that allow AI teammates to learn from enterprise environments, reason over tribal knowledge, and drive measurable business impact. You will help shape how applied science is practiced in the industry in the age of agents.
This is a high-autonomy role for a builder who wants to move beyond static benchmarks and solve the "last mile" problem of AI reliability and agency in the enterprise.
What You'll Build
You will research, design, and ship the next generation of our system architecture, focusing on:
- Agents & Tribal Knowledge Systems: Design multi-agent architectures that tackle complex, long-horizon tasks.
- Solve High-Impact Applied Science Problems: Lead the charge in identifying, scoping, and solving complex business problems using machine learning. This includes everything from improving user engagement and retention to optimizing pricing and inventory.
- Partner with Customers: Work directly with the engineering and product teams of our most strategic customers. You'll be their trusted advisor for all things machine learning, helping them adopt agentic architectures.
- End-to-End Model Development: Design, build, and deploy production-grade machine learning models for our customers using the Pavo AI platform, extending its capabilities where necessary to handle large-scale user-centric systems.
What We Are Looking For
We are looking for exceptional individuals who can operate at the frontier of applied AI research. You should be as comfortable reading a NeurIPS paper as you are debugging a distributed system.
Core Qualifications
- Experience & Impact: 4+ years of experience in Applied Science or ML Engineering, with a clear track record of shipping ML products that directly impacted top-line business metrics (e.g., retention, engagement, revenue) at scale (100M+ users).
- Production Engineering: You are an engineer first. Deep proficiency in Python/C++, experience with low-latency inference systems, and familiarity with distributed computing frameworks (Ray, Spark, Flink). You write code that survives in production, not just notebooks.
- The Full ML Lifecycle: Expertise in end-to end system design: from feature stores and real time data pipelines (Kafka/Beam) to A/B testing infrastructure and model monitoring. You understand the nuances of online vs. offline evaluation and have experience solving for feedback loops in production.
- Algorithmic Depth: Strong foundations in core ML approaches used in large-scale search/recsys (embeddings, retrieval & ranking, GNNs, bandits) combined with expertise in the frontier stack (LLMs, RL, multi agent orchestration).
- Technical Strategy: Experience defining technical roadmaps and architectural standards. You can navigate trade-offs between model complexity, serving latency, and engineering velocity.
Nice to Have
- PhD or M.S. in Computer Science, Statistics, or a related quantitative field.
- Experience at a frontier AI lab or high growth AI startup.
- Publications in top tier ML conferences (e.g., NeurIPS, ICML, ICLR, KDD, RecSys).
- Background in recommender systems, personalization, causal inference, or computational advertising.
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.