Crane Venture Partners
About SenseOn SenseOn is an AI cybersecurity platform helping organisations gain holistic visibility and control of cyber risk. We're building an AI-first go-to-market engine that blends engineering, data, and commercial execution to create pipeline and accelerate customer outcomes. The role This is a software-leaning GTM role for someone who can build, ship, and operate. You'll expand and run internal GTM systems (agent workflows, integrations, data pipelines, and internal tools) on a modern cloud stack. You'll work closely with commercial teams to turn GTM needs into reliable, secure, measurable capabilities. We value people who enjoy end-to-end ownership - defining the problem, shipping the solution, and operating it with high standards. This role is ideal for an engineer who is excited to apply technical skills to revenue outcomes, communicate clearly with non-technical stakeholders, and develop a strong understanding of cybersecurity. Working arrangement We're remote-first and welcome applications globally, including from South Africa. To collaborate effectively, this role requires meaningful working-hours overlap with UK time (typically 4+ hours on most weekdays). What you'll do Build and maintain internal GTM services and automations that support pipeline creation and customer workflows (e.g., routing, enrichment, scoring, orchestration, CRM write-backs). Extend and improve our internal agent stack (Apex) and the workflows that power it. Use Codex, Claude Code, and Gemini to accelerate delivery, while maintaining strong QA and correctness. Build and maintain production-grade integrations via APIs and event-driven patterns (e.g., webhooks). Create and maintain automated build/test/deploy pipelines (CI/CD) so changes ship safely and repeatably (e.g., GitHub Actions or equivalent). Improve reliability and observability (logs/metrics), and debug issues pragmatically when things break. Communicate clearly in writing: what you built, why it works, how to use it, and how to troubleshoot it. Build with a security-first mindset: least privilege, safe secrets handling, auditability, defensive defaults. What success looks like Useful GTM capabilities ship regularly and are adopted because they make work faster and higher quality. Changes land through a clean CI/CD pipeline with tests and safe deployment practices. Systems are secure, observable, and predictable (clear logs, measurable outcomes). Agent outputs are consistently high quality due to good prompting, deterministic code wherever possible, evaluation, guardrails, and QA and learning loops. What we're looking for (must-haves) Strong practical coding ability with clear evidence on GitHub (projects you built or meaningful contributions). Comfortable working with agentic coding workflows and modern coding assistants (Codex, Claude Code, Gemini) - and validating correctness. Experience building software that integrates with other systems: APIs, auth, webhooks, data handling. Experience with CI/CD (e.g., GitHub Actions or equivalent): automated tests, build pipelines, and deployment workflows. Familiarity with cloud-deployed services: debugging, logs/metrics, reliability basics. Clear written communication and good judgement. Security-first mindset and curiosity to learn the cybersecurity industry. Nice-to-haves Python (or another backend language) used in production. Infrastructure/deployment tooling exposure (even at a basic level). Experience building internal tools for commercial teams. Exposure to security fundamentals (IAM concepts, threat modelling, secure defaults). How to apply Please include: Link to your GitHub profile and 1-3 repos you're proud of. A brief note explaining your CI/CD setup on one of those repos (what runs on PR, what runs on merge, how deployments work). A short example of how you've used AI coding tools (what you built, and how you tested/validated it). Optional: a short technical write-up you've written (README/docs/blog) that explains a system clearly. Interview process (what to expect) Initial screen: experience, motivation, and quick technical discussion. Repo walkthrough: you'll walk us through one GitHub project, including CI/CD and trade-offs. Practical exercise: a small build task focused on correctness, security-first thinking, and clear documentation. Final conversation: alignment on ownership, communication, and growth. Benefits Competitive salary Unlimited holiday allowance Learning and development investment (certs, conferences, etc) A Mac laptop Enhanced pension Private healthcare with vitality offering rewards and discounts from Amazon Prime to Gym Membership Belong at SenseOn: At SenseOn, we define Talent as employees who are customer obsessed, pursuing excellence. They are :lion_face: courageous, good people, doing good things, powering our :rocket: rocketship. If this resonates with you, then you will always belong. Nothing else matters. We are an Equal Opportunity Employer and do not discriminate against any qualified employee or applicant. Difference is what makes us stronger.
About SenseOn SenseOn is an AI cybersecurity platform helping organisations gain holistic visibility and control of cyber risk. We're building an AI-first go-to-market engine that blends engineering, data, and commercial execution to create pipeline and accelerate customer outcomes. The role This is a software-leaning GTM role for someone who can build, ship, and operate. You'll expand and run internal GTM systems (agent workflows, integrations, data pipelines, and internal tools) on a modern cloud stack. You'll work closely with commercial teams to turn GTM needs into reliable, secure, measurable capabilities. We value people who enjoy end-to-end ownership - defining the problem, shipping the solution, and operating it with high standards. This role is ideal for an engineer who is excited to apply technical skills to revenue outcomes, communicate clearly with non-technical stakeholders, and develop a strong understanding of cybersecurity. Working arrangement We're remote-first and welcome applications globally, including from South Africa. To collaborate effectively, this role requires meaningful working-hours overlap with UK time (typically 4+ hours on most weekdays). What you'll do Build and maintain internal GTM services and automations that support pipeline creation and customer workflows (e.g., routing, enrichment, scoring, orchestration, CRM write-backs). Extend and improve our internal agent stack (Apex) and the workflows that power it. Use Codex, Claude Code, and Gemini to accelerate delivery, while maintaining strong QA and correctness. Build and maintain production-grade integrations via APIs and event-driven patterns (e.g., webhooks). Create and maintain automated build/test/deploy pipelines (CI/CD) so changes ship safely and repeatably (e.g., GitHub Actions or equivalent). Improve reliability and observability (logs/metrics), and debug issues pragmatically when things break. Communicate clearly in writing: what you built, why it works, how to use it, and how to troubleshoot it. Build with a security-first mindset: least privilege, safe secrets handling, auditability, defensive defaults. What success looks like Useful GTM capabilities ship regularly and are adopted because they make work faster and higher quality. Changes land through a clean CI/CD pipeline with tests and safe deployment practices. Systems are secure, observable, and predictable (clear logs, measurable outcomes). Agent outputs are consistently high quality due to good prompting, deterministic code wherever possible, evaluation, guardrails, and QA and learning loops. What we're looking for (must-haves) Strong practical coding ability with clear evidence on GitHub (projects you built or meaningful contributions). Comfortable working with agentic coding workflows and modern coding assistants (Codex, Claude Code, Gemini) - and validating correctness. Experience building software that integrates with other systems: APIs, auth, webhooks, data handling. Experience with CI/CD (e.g., GitHub Actions or equivalent): automated tests, build pipelines, and deployment workflows. Familiarity with cloud-deployed services: debugging, logs/metrics, reliability basics. Clear written communication and good judgement. Security-first mindset and curiosity to learn the cybersecurity industry. Nice-to-haves Python (or another backend language) used in production. Infrastructure/deployment tooling exposure (even at a basic level). Experience building internal tools for commercial teams. Exposure to security fundamentals (IAM concepts, threat modelling, secure defaults). How to apply Please include: Link to your GitHub profile and 1-3 repos you're proud of. A brief note explaining your CI/CD setup on one of those repos (what runs on PR, what runs on merge, how deployments work). A short example of how you've used AI coding tools (what you built, and how you tested/validated it). Optional: a short technical write-up you've written (README/docs/blog) that explains a system clearly. Interview process (what to expect) Initial screen: experience, motivation, and quick technical discussion. Repo walkthrough: you'll walk us through one GitHub project, including CI/CD and trade-offs. Practical exercise: a small build task focused on correctness, security-first thinking, and clear documentation. Final conversation: alignment on ownership, communication, and growth. Benefits Competitive salary Unlimited holiday allowance Learning and development investment (certs, conferences, etc) A Mac laptop Enhanced pension Private healthcare with vitality offering rewards and discounts from Amazon Prime to Gym Membership Belong at SenseOn: At SenseOn, we define Talent as employees who are customer obsessed, pursuing excellence. They are :lion_face: courageous, good people, doing good things, powering our :rocket: rocketship. If this resonates with you, then you will always belong. Nothing else matters. We are an Equal Opportunity Employer and do not discriminate against any qualified employee or applicant. Difference is what makes us stronger.
Crane Venture Partners
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.
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.