Deep Science Ventures एक वेंचर क्रिएशन कंपनी है जो आशाजनक शोध अवसरों को संस्थापक क्षमता वाले वैज्ञानिकों के साथ जोड़कर वैज्ञानिक ज्ञान को उच्च-प्रभाव वाले स्टार्टअप में बदलती है। इसका काम कृषि, कंप्यूटेशन, जलवायु और फार्मा क्षेत्रों में फैला है, जिसमें पुनर्योजी खाद्य प्रणालियों, कंप्यूटेशनल सफलताओं, वैश्विक तापवृद्धि के समाधानों और रोगों का उपचार करने वाली थेरेपी पर ध्यान केंद्रित किया जाता है। कंपनी के अनुसार, उसने 42 कंपनियां शुरू की हैं, 37 परियोजनाएं विकासाधीन हैं, 160 मिलियन डॉलर की फंडिंग आकर्षित की है और उसके पोर्टफोलियो का मूल्यांकन 430 मिलियन डॉलर है।
Co-Founder & CTO, AI Drug Discovery (London Hybrid)
Lead Deep Science Ventures’ AI platform for forecasting drug-development and clinical-trial outcomes. Shape the evaluation framework, production technology, and founding technical team.
विवरण
- Own and advance the existing platform.
- Set the technical roadmap and deliver platform improvements.
- Define prediction tasks and connect decision-time evidence with later outcomes.
- Develop, assess, and compare modelling strategies.
- Measure predictive performance and calibration.
- Determine where the system performs reliably and where its predictions remain limited.
- Resolve evaluation risks involving leakage, label quality, and data contamination.
- Recruit the initial technical team after funding is secured.
- Support fundraising by communicating the technical approach, evidence, limitations, and development roadmap to investors.
- Build the platform directly for at least the first year.
- Partner with the biology and commercial founders to set the platform’s direction.
- Help improve the predictability of therapeutic development and shape which treatments move forward.
आवश्यकताएँ
- A recognised technical record from Google DeepMind, OpenAI, Anthropic, Meta AI, or a comparable frontier AI laboratory.
- Experience leading a team or a substantial technical programme.
- Willingness to remain hands-on and build the platform personally.
- Experience designing evaluations and benchmarks, including label definition, reliability checks, and leakage-resistant data splits.
- Strong knowledge of calibration, confidence intervals, base rates, and the constraints of small or noisy datasets.
- Broad expertise spanning hierarchical and Bayesian inference, classical statistics, graph learning including graph neural networks, probabilistic graphical models, and learned calibration.
- Experience operating production LLM systems with agent orchestration, tool integrations, context management, and regression-detection evaluation systems.
- Experience running multiple model providers in production is advantageous.
- Hands-on technical leadership, including reviewing and testing AI-generated code and automated pipelines.
- Ability to communicate uncertainty and limitations and identify when the available evidence is insufficient.
- Clear technical communication with collaborators and technical investors.
- Availability to move into a full-time role from spin-out.
- UK-based candidates preferred, though relocation or remote work may be possible for the right person.
- Must not be building, or have recently built, a company on the same foundation of agentic reasoning, knowledge graphs, and hypothesis generation for drug discovery.
- Experience with biology or clinical data is preferred.
- Previous founder experience is preferred.
- Reviewable research, software, or open-source work is preferred.
- Experience building evaluation or benchmarking infrastructure is preferred.
लाभ
- A genuine co-founder equity stake.
- An operational platform and an existing published scientific result.
- Technical authority from the outset, including architectural decisions.
- Access to Deep Science Ventures’ international network of investors, advisors, and industrial partners.
- Access to proprietary venture-building tools, resources, and processes.
- Ongoing post-spinout support with fundraising, commercial partnerships, recruitment, and team building.
- A full-time commitment from spin-out.
- Flexible location, with remote work possible for the right person.