Full-Time
Senior Security Engineer
AI Safety
Isomorphic Labs
201-500 employees
AI-powered drug discovery and development
No salary listed
London, UK
Hybrid
Three days in the office per week (Tuesday, Wednesday, and one other day).
Bachelor's, Master's, PhD
 IT & Security (1) |
|---|
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- AI/ML Engineering: Deep conceptual and practical understanding of deep learning frameworks (e.g., JAX, PyTorch, TensorFlow), large-scale cloud training or inference infrastructure, and LLM ecosystems.
- Adversarial ML Mindset: Strong familiarity with AI security threat vectors (prompt injection, model inversion, data poisoning) and frameworks like OWASP Top 10 for LLMs or MITRE ATLAS.
- Agentic Frameworks Security: Proven experience securing agentic frameworks (ADK, MCP), agentic first infrastructure and implementing robust agent-to-agent (A2A) identity and access controls.
- Infrastructure Security: Solid proficiency in cloud platform security (GCP preferred), including container security, multi-cloud/SaaS integrations, and strict network isolation boundaries.
- Pragmatic Risk Assessment: Proven ability to define and execute pragmatic mitigation strategies that balance a rigorous security posture with the high-velocity needs of a world-class scientific research organization.
- Software Development & Automation: Ability to write production-grade code (Python preferred) to build custom security tooling, automate telemetry collection, or enforce policy layers.
- Translational Communication: Exceptional skills in navigating ambiguity, building trusted relationships with AI researchers, and clearly translating complex machine learning risks into actionable engineering tasks for leadership.
- Adversarial Threat Modeling: Conduct cutting-edge threat modeling focused on AI/ML vulnerabilities, engineering a comprehensive risk management framework tailored to an AI-powered life sciences platform.
- Model Artifacts Protection: Partner with AI teams to establish a granular inventory, classification and protection framework for ML models’ weights, code, and training data.
- Agentic AI & LLM Guardrails: Design and deploy scalable guardrails, robust sandboxing, and real-time monitoring controls for our extensive LLM ecosystem, including ADK, MCP, and autonomous agentic workflows.
- Collaborative Security Engineering: Partner with ML researchers and platform engineers to design and implement robust security controls across the entire ML lifecycle—from data ingestion and training pipeline integrity to inference runtime controls.
- Model-Driven Incident Response: Act as the primary technical expert for AI security incidents, deploying advanced machine learning techniques to automate threat hunting, anomaly detection, and signatureless mitigation specific to LLMs.
- Compliance & Governance Automation: Bridge the gap between complex AI architectures and emerging global regulations (e.g., EU AI Act) by automating risk posture monitoring and continuous compliance metrics.
- AI Safety Frameworks: Pioneer the practical implementation of Isomorphic Labs' AI Safety standards, working in close coordination with Legal, Compliance, and external partners like Google DeepMind.
- AI Red Teaming: Experience conducting empirical vulnerability research or executing simulated attacks against LLMs, agent networks, and core ML backends.
- Regulated Environments: Prior exposure to BioTech, Pharma, and/or Deep Tech industries where data integrity, intellectual property protection, and regulatory compliance (GxP) are paramount.
- Education & Background: BSc, MSc, or PhD in Computer Science, Machine Learning, Cybersecurity, or a related quantitative field.
- Advanced Certifications: Relevant security or cloud credentials (e.g., OSCP, Professional Cloud Security Engineer) or specialized training in ML security.
Isomorphic Labs uses artificial intelligence to speed up drug discovery and development. It applies machine learning and computational methods—building predictive and generative models—to accelerate how drugs are designed and how medical research is conducted. The company differentiates itself by combining the AI breakthroughs from Google DeepMind with Alphabet backing, enabling fast experimentation and scaling across biology while maintaining startup-style agility. Its goal is to bring safer, more effective therapies to market more quickly by advancing AI-driven medicine using deep learning, reinforcement learning, and other advanced techniques.
Company Size
201-500
Company Stage
Series B
Total Funding
$2.7B
Headquarters
London, United Kingdom
Founded
2021
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Simplify's Take
What believers are saying
- May 12, 2026 Series B raised $2.1 billion from Thrive, Alphabet, MGX, Temasek.
- Capital funds IsoDDE expansion and hiring across London, Cambridge, and Lausanne.
- Lilly, Novartis, and Johnson Johnson collaborations still validate pharma demand for Isomorphic's platform.
What critics are saying
- Reuters reported first clinical trials slipped from end-2025 to end-2026.
- Pfizer backed Chai Discovery in June 2026, validating faster-moving AI-drug competitors.
- If IsoDDE fails Phase I by 2027, Isomorphic remains a software thesis, not a drug company.
What makes Isomorphic Labs unique
- DeepMind lineage gives Isomorphic Labs AlphaFold-era biology models and Alphabet backing.
- IsoDDE reportedly beat AlphaFold 3 on protein-ligand generalization in February 2026.
- Thrive Capital led both the March 2025 and May 2026 financings.
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Benefits
Hybrid Work Options
Growth & Insights and Company News
6 month growth
↓ -1%1 year growth
↑ 0%2 year growth
↑ 1%Isomorphic Labs is an AI-first drug design and development company that transforms drug discovery to design and evaluate new medicines.
UK Biotech Funding Hits a Five-Year High: What Is Driving the Recovery?UK biotech investment has just recorded its strongest quarter in five years. New figures from the BioIndustry Association (BIA) show that UK biotech companies secured £2.11 billion in equity financing between April and June 2026, including a record £2.05 billion in venture capital. After several years in which access to capital has remained one of the sector's biggest challenges, the figures provide a significant indication that investor confidence is beginning to return. But behind the headline number is a more complicated - and potentially more important - story about where investment is flowing and what that could mean for the next generation of UK life sciences companies. A landmark quarter for UK biotech. The second quarter of 2026 delivered the strongest quarterly venture capital total recorded by UK biotech in five years. A substantial proportion came from one company. London-based Isomorphic Labs, the AI-driven drug discovery company spun out of Google DeepMind, completed a $2.1 billion Series B financing round, valued at approximately £1.6 billion in the BIA figures. The scale of the investment reflects the extraordinary interest surrounding the convergence of artificial intelligence and drug discovery, with Isomorphic Labs developing AI systems designed to accelerate the identification and development of new medicines. The round inevitably transformed the headline figures for the quarter. But removing it does not remove the wider recovery. The recovery goes beyond one megadeal. Excluding Isomorphic Labs, UK biotech companies still raised £498 million in venture capital during the quarter. That compares with £279 million during the same period in 2025. More importantly, investment is beginning to appear across different stages of company growth. Eight seed deals were completed during the quarter, with an average value of £6.4 million. Series A companies collectively raised around £190 million, while later-stage Series B+ companies secured approximately £225 million. There has also been movement in the crucial £10 million to £25 million funding range. Twice as many companies raised rounds of this size during the first half of 2026 as did across the entirety of 2025. For the UK life sciences ecosystem, this matters. Large financing rounds attract attention, but a sustainable biotech sector also depends on companies being able to access capital as they move from early research into clinical development, commercialisation and scale-up. The UK leads europe for biotech investment. The figures also reinforce the UK's position within the European biotech market. UK companies attracted 61% of the £3.3 billion in venture capital invested across European biotech during the second quarter of 2026. That places the UK firmly at the centre of European biotech investment at a time when countries across the continent are competing to attract scientific talent, companies and international capital. The strength of the UK ecosystem has traditionally been associated with established clusters around London, Oxford and Cambridge. But successful life sciences companies increasingly emerge from a much broader national network of universities, hospitals, laboratories, manufacturers and innovation organisations. Regional ecosystems, including the West Midlands, form an important part of that pipeline, particularly across diagnostics, medical technology, advanced manufacturing, healthcare innovation and university spinouts. For the UK to maintain its position, the challenge is not simply producing promising science. It is creating the conditions for companies to remain, grow and raise substantial capital in the UK. Public Markets Remain the Missing Piece Despite the strength of private investment, the recovery is far from complete. Public markets remain notably subdued. Follow-on financing for listed UK biotech companies reached £58 million during the second quarter, up from £36 million in Q1 and £15 million during the same quarter last year. Yet no UK biotech IPOs have taken place so far in 2026. That creates an important divide within the market. Private investors are showing renewed willingness to back promising companies, but the public financing environment has yet to experience the same recovery. For growing biotech businesses, that matters because venture capital cannot support every stage of development indefinitely. A healthy ecosystem ultimately requires multiple routes to capital, from seed investment and venture funding through to institutional finance, public markets, partnerships and acquisitions. What comes next? The second quarter does not mean the difficult funding environment facing biotech has disappeared. Investment remains selective, public markets remain weak and raising capital continues to be challenging for many companies. But the direction of travel is encouraging. The significance of the latest figures is not simply that one exceptional company raised an exceptional amount of money. It is that underneath that deal, investment activity is beginning to broaden across different stages of the UK biotech ecosystem. For founders, investors and researchers, the question now is whether that momentum can be sustained. It is a particularly timely question ahead of Life Sciences Week 2026, taking place from 21-25 September. Funding, investment, commercialisation and scale-up will form part of the wider conversation as researchers, businesses, investors, healthcare leaders and policymakers come together across Birmingham, the West Midlands and beyond. After years in which the UK life sciences funding debate has often focused on what is missing, the latest figures provide something different: evidence that capital is beginning to move again. The challenge now is turning one strong quarter into sustained growth. Sources: BioIndustry Association, UK biotech financing April-June 2026; Isomorphic Labs.
Its approach to bioresilience. Google DeepMind and Isomorphic Labs are sharing its joint approach to bioresilience and AI models. Xcobean Tech Desk Xcobean Systems Google DeepMind and Isomorphic Labs have recently announced their collaboration on a new approach to bioresilience, focusing on the development of artificial intelligence (AI) models that can better withstand and adapt to biological challenges. This partnership aims to enhance the robustness of AI systems in healthcare and biotechnology sectors. For businesses in Kenya or East Africa, this development underscores the importance of integrating resilient AI solutions into their operations, particularly in areas such as public health, agriculture, and environmental monitoring. Companies should consider investing in research and partnerships that align with global advancements in bioresilience to ensure they can effectively address local biological challenges while maintaining operational stability. Tech News Analysis
Isomorphic Labs raised $2.1 billion in Series B funding led by Thrive Capital, with backing from Alphabet, Google Ventures, MGX, Temasek, CapitalG and the UK Sovereign AI Fund. The Google DeepMind spinout will use the capital to expand its AI drug design platform and advance its pipeline towards clinical testing. The company now expects its first trials by the end of 2026, later than previously targeted. Isomorphic's platform aims to explore historically difficult-to-drug biology, including induced-fit interactions and cryptic binding pockets. The funding reflects growing investor appetite for end-to-end AI drug discovery infrastructure. Genesis Molecular AI and Incyte expanded their collaboration to more than $1 billion, whilst Chai Discovery signed a licensing deal with Pfizer. However, clinical validation of AI-discovered compounds remains limited.
SandboxAQ brings physics-based AI drug discovery to Claude chatbot. The Google spinout thinks the problem isn't better models, but making them accessible through conversation instead of code May 20, 2026 Drug discovery costs billions and takes a decade per viable molecule. Most candidates still fail. While AI startups have built better tools for this process, they've mostly served researchers who already have the technical skills to use complex software. SandboxAQ thinks the real problem isn't the models themselves. It's making them easy to use. The company has partnered with Anthropic to put its scientific AI directly into Claude, letting researchers run powerful drug discovery simulations through simple conversation instead of specialized computing setups. What makes SandboxAQ different from other AI drug companies. Founded five years ago as an Alphabet spinout, SandboxAQ has former Google CEO Eric Schmidt as chairman. The company has raised over $950 million and runs several business lines, including cybersecurity. But its most interesting work involves large quantitative models, or LQMs. Unlike typical AI trained on text patterns, these models are "physics-grounded" - built on actual rules of the physical world. They can run quantum chemistry calculations and simulate how molecules move and react at the atomic level. This matters because it tells researchers how candidate drugs might behave before anyone touches a test tube. Targeting the $50 trillion quantitative economy. "Trained on real-world lab data and scientific equations, LQMs are AI models engineered for the quantitative economy, a $50+ trillion sector spanning biopharma, financial services, energy, and advanced materials," the company said in a release. That positioning sets SandboxAQ apart from well-funded competitors like Chai Discovery and Isomorphic Labs, which focus on building better scientific models. SandboxAQ cares more about who can actually use the technology. "For the first time, we have a frontier [quantitative] model on a frontier LLM that someone can access in natural language," Nadia Harhen, SandboxAQ's general manager of AI simulation, told TechCrunch. Solving problems other software can't handle. Previously, using SandboxAQ's models required users to provide their own computing infrastructure. The Claude integration removes that barrier. SandboxAQ's customers are typically computational scientists, research scientists, or experimentalists at large pharmaceutical or industrial companies. They're hunting for new materials that can become actual products. "Our customers come to us because they've tried all the other software out there, and the complexity of their problem is such that it didn't work or didn't yield positive results for them when that translation went to take place in the real world," Harhen said. The move reflects a broader shift in enterprise AI - from building more powerful models to making existing ones more accessible to non-technical users. In drug discovery, where failed experiments cost millions, that accessibility could determine which tools actually get used in the lab.