Research Scientist/Engineer, Biological and Chemical Models

AI Security Institute
London
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist, Quantitative Biosciences

Machine Learning Manager, London

Machine Learning Research Scientist - PhD, NLP, LLM

Research Software Engineer

Data Scientist

Machine Learning Engineer

About the AI Security Institute

The AI Security Institute is the largest team in a government dedicated to understanding AI capabilities and risks in the world. Our mission is to equip governments with an empirical understanding of the safety of advanced AI systems. We conduct research to understand the capabilities and impacts of advanced AI and develop and test risk mitigations. We focus on risks with security implications, including the potential of AI to assist with the development of chemical and biological weapons, how it can be used to carry out cyber-attacks, enable crimes such as fraud, and the possibility of loss of control.

The risks from AI are not sci-fi, they are urgent. By combining the agility of a tech start-up with the expertise and mission-driven focus of government, were building a unique and innovative organisation to prevent AIs harms from impeding its potential.

Research Scientist/Engineer, Biological and Chemical Models | AI Security Institute

London, UK

The AI Security Institute (AISI), launched at the 2023 Bletchley Park AI Safety Summit, is the worlds first state-backed organisation dedicated to advancing AI security for the public interest. Our mission is to assess and mitigate risks from frontier AI systems, including cyber attacks on critical infrastructure, AI-enhanced chemical and biological threats, large-scale societal disruptions, and potential loss of control over increasingly powerful AI. In just one year, weve assembled one of the largest and most respected research teams, featuring renowned scientists and senior researchers from leading AI labs such as Anthropic, DeepMind, and OpenAI.

At AISI, were building the premier institution for impacting both technical AI safety and AI governance. We conduct cutting-edge research, develop novel evaluation tools, and provide crucial insights to governments, companies, and international partners. By joining us, youll collaborate with the brightest minds in the field, directly shape global AI policies, and tackle complex challenges at the forefront of technology and ethics. Whether youre a researcher, engineer, or policy expert, at AISI, youre not just advancing your career - youre positioned to have significant impact in the age of artificial intelligence.

ROLE SUMMARY

  • Join a small, talent-driven, multidisciplinary team evaluating state-of-the-art machine learning models for engineering biology.
  • Setup and deploy frontier models and evaluate these models in line with AISIs research objectives.
  • Write code to assess capabilities of frontier models in fields such as protein design, structure prediction and biological foundation models.
  • Explore research questions at the intersection of AI and biosecurity and help communicate results to inform wider cross-Government efforts.

PERSON SPECIFICATION

We strongly encourage you to apply even if you feel you only meetsomeof the criteria listed here.

Essential Criteria

  • Background in machine learning, having worked directly on training, tuning or evaluating machine learning models using PyTorch or similar.
  • Experience working on biological (frontier) AI models, such as protein or genomic language models, structure prediction (AlphaFold) or protein design models (RFDiffusion)
  • Proficient at coding in Python

Desirable Criteria

  • A background in computational biology, with understanding of the provenance and limitations of omics data, and the challenges of building predictive models using these data types.
  • Strong background in biology or chemistry, with an understanding of protein biochemistry and experimental assays used to validate protein design.
  • Good scientific research experience, and a motivation to follow research best practices to solve open questions at the intersection of AI and biosecurity.
  • Experience writing production-level code that is scalable, robust and easy to maintain, ideally in Python.
  • Experience working in small cross-functional teams, including both scientists and engineers.
  • Experience in communicating technical work to a mixture of technical and non-technical audiences.

Clearance Criteria

Whilst AISI often encourages applications from individuals of any nationality, due to the unique nature of this role and the elevated security clearances that would be required once in post, we can only accept individuals capable of meeting DV criteria which is largely restricted to UK nationals.

Salary & Benefits

We are hiring individuals at all ranges of seniority and experience within this research unit, and this advert allows you to apply for any of the roles within this range. Your dedicated talent partner will work with you as you move through our assessment process to explain our internal benchmarking process.

  • Level 3 - Total Package £65,000 - £75,000inclusiveof a base salary £35,720 plus additional technical talent allowance of between £29,280 - £39,280
  • Level 4 - Total Package £85,000 - £95,000inclusiveof a base salary £42,495 plus additional technical talent allowance of between £42,505 - £52,505
  • Level 5 - Total Package £105,000 - £115,000inclusiveof a base salary £55,805 plus additional technical talent allowance of between £49,195 - £59,195
  • Level 6 - Total Package £125,000 - £135,000inclusiveof a base salary £68,770 plus additional technical talent allowance of between £56,230 - £66,230
  • Level 7 - Total Package £145,000inclusiveof a base salary £68,770 plus additional technical talent allowance of £76,230

Key Words:computational protein design, structural biology

J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.