An innovative AI company seeks a Founding Applied Scientist to develop AI systems that drive business impact. This high-autonomy role involves research, design, and building complex systems for enterprise environments. The ideal candidate has over 4 years of experience in Applied Science or ML Engineering, proficiency in Python/C++, and expertise in the full ML lifecycle. You'll lead projects that shape applied science in the industry, tackling significant challenges in AI reliability and agency.
May 31, 2026
Full time
An innovative AI company seeks a Founding Applied Scientist to develop AI systems that drive business impact. This high-autonomy role involves research, design, and building complex systems for enterprise environments. The ideal candidate has over 4 years of experience in Applied Science or ML Engineering, proficiency in Python/C++, and expertise in the full ML lifecycle. You'll lead projects that shape applied science in the industry, tackling significant challenges in AI reliability and agency.
Crane Venture Partners seeks an Applied Scientist for their London office. You will orchestrate applied research in tribal-knowledge generation, turning ambiguous production behavior into actionable solutions. Collaborate closely with engineers and researchers to develop and enhance systems that respond intelligently to organizational needs. The role demands a strong background in applied research and machine learning, encouraging publication and ownership of significant scientific projects.
May 31, 2026
Full time
Crane Venture Partners seeks an Applied Scientist for their London office. You will orchestrate applied research in tribal-knowledge generation, turning ambiguous production behavior into actionable solutions. Collaborate closely with engineers and researchers to develop and enhance systems that respond intelligently to organizational needs. The role demands a strong background in applied research and machine learning, encouraging publication and ownership of significant scientific projects.
Applied Scientist (Tribal Knowledge) Lead the science of compiling an organization's tribal knowledge into a verifiable artifact London / SF • Full-time • Research 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 an Applied Scientist at Pavo, you will lead the science track of tribal-knowledge generation. You'll work on the open problems that sit between today's RAG and tomorrow's organizationally-aware agents - and turn them into shipped, evidence-backed improvements to the production system. This is applied research in the truest sense: the questions arise from real production behavior, the answers must improve it, and the cycle from interesting finding to shipped change is days, not quarters. The questions themselves are also publishable - most sit at or beyond the current literature. This is a senior, individual-contributor role. Everyone on the team joins as a Member of Technical Staff - with the scope, autonomy, and end-to-end ownership that title implies. What You'll Work On Retrieval over Heterogeneous Private Evidence: How an agent should traverse an organization's source code, structured data, internal documents, and conversations to assemble the evidence required to compile knowledge. Verifiability of Open-Ended Generation: What it means for an agent-produced knowledge artifact to be trustworthy - beyond precision-only validation of individual facts. Evaluation of Multi-Stage Agentic Pipelines: Benchmarks and instrumentation that localize quality gains to the responsible stage, without leaking the answer key into the pipeline being measured. Reliability & Variance: Characterizing and reducing run-to-run variance in stochastic synthesis, so knowledge artifacts can be released with the same confidence as deterministic software. Continual Update & Conflict Resolution: How a compiled knowledge artifact should evolve as the underlying organization changes - surfacing conflict and accruing authority and temporal validity. Publication: Internal findings as decision-grade memos; external results as papers, talks, or technical reports - wherever the work advances the field. What We Are Looking For We are looking for an applied researcher who turns messy production behavior into questions, and questions into shipped, evidence-backed change. Core Qualifications Senior Track Record: Years of applied-research or ML experience (typically 8+ in industry, or a PhD plus a strong applied-research record), including work you drove end-to-end that held up under scrutiny - the scientist others bring their hardest, most ambiguous problems to. Working Understanding of Agentic Systems: You know how tool use, multi-turn execution, context limits, and structured outputs behave in practice - even if you haven't built a production agent itself. Strong Retrieval Fundamentals: Fluency in dense and sparse retrieval, reranking, query understanding, and IR-style evaluation. Many of the open problems here are dressed-up retrieval problems. Experimental Discipline: You've designed and run ablations that survive scrutiny; you treat n=1 with the suspicion it deserves; you know the difference between a result that explains the past and one that predicts the future. Familiarity with the Hallucination & RAG-Eval Literature: At a level where you can identify when a published benchmark or method has structural limitations. Production Intuition: You can read messy run logs and formulate the question hiding inside them. Strong Technical Writing: You can produce a finding another scientist trusts, and a script the engineering team can run. Nice to Have Publications in agents, RAG, IR, hallucination evaluation, knowledge integration, or continual learning. Hands-on experience designing benchmarks or evaluation harnesses for open-ended generation. Familiarity with conflict-resolution, record-linkage, or entity-resolution literature - these surface as adjacent problems in tribal knowledge. PhD in ML / NLP / IR, or an equivalent applied-research track record in industry. Why Join Us Foundational Work: The private knowledge layer will reshape how AI agents operate inside organizations. The problems are real and at the edge of the field. Short Loop: Work directly with the engineering lead and the founders. Finding to recommendation to shipped change is days, not quarters. Real Ownership of the Science Agenda: In a small, technically deep team. Your name will be on the work. Publication Encouraged: Including external - papers, talks, and technical reports where the work advances the field. Pavo is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
May 31, 2026
Full time
Applied Scientist (Tribal Knowledge) Lead the science of compiling an organization's tribal knowledge into a verifiable artifact London / SF • Full-time • Research 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 an Applied Scientist at Pavo, you will lead the science track of tribal-knowledge generation. You'll work on the open problems that sit between today's RAG and tomorrow's organizationally-aware agents - and turn them into shipped, evidence-backed improvements to the production system. This is applied research in the truest sense: the questions arise from real production behavior, the answers must improve it, and the cycle from interesting finding to shipped change is days, not quarters. The questions themselves are also publishable - most sit at or beyond the current literature. This is a senior, individual-contributor role. Everyone on the team joins as a Member of Technical Staff - with the scope, autonomy, and end-to-end ownership that title implies. What You'll Work On Retrieval over Heterogeneous Private Evidence: How an agent should traverse an organization's source code, structured data, internal documents, and conversations to assemble the evidence required to compile knowledge. Verifiability of Open-Ended Generation: What it means for an agent-produced knowledge artifact to be trustworthy - beyond precision-only validation of individual facts. Evaluation of Multi-Stage Agentic Pipelines: Benchmarks and instrumentation that localize quality gains to the responsible stage, without leaking the answer key into the pipeline being measured. Reliability & Variance: Characterizing and reducing run-to-run variance in stochastic synthesis, so knowledge artifacts can be released with the same confidence as deterministic software. Continual Update & Conflict Resolution: How a compiled knowledge artifact should evolve as the underlying organization changes - surfacing conflict and accruing authority and temporal validity. Publication: Internal findings as decision-grade memos; external results as papers, talks, or technical reports - wherever the work advances the field. What We Are Looking For We are looking for an applied researcher who turns messy production behavior into questions, and questions into shipped, evidence-backed change. Core Qualifications Senior Track Record: Years of applied-research or ML experience (typically 8+ in industry, or a PhD plus a strong applied-research record), including work you drove end-to-end that held up under scrutiny - the scientist others bring their hardest, most ambiguous problems to. Working Understanding of Agentic Systems: You know how tool use, multi-turn execution, context limits, and structured outputs behave in practice - even if you haven't built a production agent itself. Strong Retrieval Fundamentals: Fluency in dense and sparse retrieval, reranking, query understanding, and IR-style evaluation. Many of the open problems here are dressed-up retrieval problems. Experimental Discipline: You've designed and run ablations that survive scrutiny; you treat n=1 with the suspicion it deserves; you know the difference between a result that explains the past and one that predicts the future. Familiarity with the Hallucination & RAG-Eval Literature: At a level where you can identify when a published benchmark or method has structural limitations. Production Intuition: You can read messy run logs and formulate the question hiding inside them. Strong Technical Writing: You can produce a finding another scientist trusts, and a script the engineering team can run. Nice to Have Publications in agents, RAG, IR, hallucination evaluation, knowledge integration, or continual learning. Hands-on experience designing benchmarks or evaluation harnesses for open-ended generation. Familiarity with conflict-resolution, record-linkage, or entity-resolution literature - these surface as adjacent problems in tribal knowledge. PhD in ML / NLP / IR, or an equivalent applied-research track record in industry. Why Join Us Foundational Work: The private knowledge layer will reshape how AI agents operate inside organizations. The problems are real and at the edge of the field. Short Loop: Work directly with the engineering lead and the founders. Finding to recommendation to shipped change is days, not quarters. Real Ownership of the Science Agenda: In a small, technically deep team. Your name will be on the work. Publication Encouraged: Including external - papers, talks, and technical reports where the work advances the field. Pavo is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
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.
May 30, 2026
Full time
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.