Research Scientist/Research Engineer

AI Safety Institute
London
2 months ago
Applications closed

Related Jobs

View all jobs

Research Scientist, Machine Learning (PhD)

Research Scientist - Large Language Model Post-Training New London, England, United Kingdom · L[...]

Research Scientist (Machine Learning), London London

Machine Learning Research Scientist - PhD, NLP, LLM

Machine Learning Research Scientist | Generative Models | Protein Design | Deep Learning | Pyth[...]

Senior Data Scientist (Document Search)

Find out more about the daily tasks, overall responsibilities, and required experience for this opportunity by scrolling down now.Role DescriptionThe AI Safety Institute research unit is looking for exceptionally motivated and talented people to join its Safeguard Analysis Team.

Interventions that secure a system from abuse by bad actors will grow in importance as AI systems become more advanced and integrated into society. The AI Safety Institute’s Safeguard Analysis Team researches such interventions, which it refers to as 'safeguards', evaluating protections used to secure current frontier AI systems and considering what measures could and should be used to secure such systems in the future.

The Safeguard Analysis Team takes a broad view of security threats and interventions. It's keen to hire researchers with expertise developing and analysing attacks and protections for systems based on large language models, but is also keen to hire security researchers who have historically worked outside of AI, such as in - non-exhaustively - computer security, information security, web technology policy, and hardware security. Diverse perspectives and research interests are welcomed.

The Team seeks people with skillsets leaning in the direction of either or both of Research Scientist and Research Engineer, recognising that some technical staff may prefer work that spans or alternates between engineering and research responsibilities. The Team's priorities include research-oriented responsibilities – like assessing the threats to frontier systems and developing novel attacks – and engineering-oriented ones, such as building infrastructure for running evaluations.

In this role, you’ll receive mentorship and coaching from your manager and the technical leads on your team. You'll also regularly interact with world-famous researchers and other incredible staff, including alumni from Anthropic, DeepMind, OpenAI and ML professors from Oxford and Cambridge.

In addition to Junior roles, Senior, Staff and Principal RE positions are available for candidates with the required seniority and experience.

Person SpecificationYou may be a good fit if you have

some

of the following skills, experience and attitudes:

Experience working on machine learning, AI, AI security, computer security, information security, or some other security discipline in industry, in academia, or independently.

Experience working with a world-class research team comprised of both scientists and engineers (e.g. in a top-3 lab).

Red-teaming experience against any sort of system.

Strong written and verbal communication skills.

Comprehensive understanding of large language models (e.g. GPT-4). This includes both a broad understanding of the literature, as well as hands-on experience with things like pre-training or fine-tuning LLMs.

Extensive Python experience, including understanding the intricacies of the language, the good vs. bad Pythonic ways of doing things and much of the wider ecosystem/tooling.

Ability to work in a self-directed way with high agency, thriving in a constantly changing environment and a steadily growing team, while figuring out the best and most efficient ways to solve a particular problem.

Bring your own voice and experience but also an eagerness to support your colleagues together with a willingness to do whatever is necessary for the team’s success and find new ways of getting things done.

Have a sense of mission, urgency, and responsibility for success, demonstrating problem-solving abilities and preparedness to acquire any missing knowledge necessary to get the job done.

Writing production quality code.

Improving technical standards across a team through mentoring and feedback.

Designing, shipping, and maintaining complex tech products.

Salary & BenefitsWe are hiring individuals at all ranges of seniority and experience within the research unit, and this advert allows you to apply for any of the roles within this range. We will discuss and calibrate with you as part of the process. The full range of salaries available is as follows:

L3: £65,000 - £75,000

L4: £85,000 - £95,000

L5: £105,000 - £115,000

L6: £125,000 - £135,000

L7: £145,000

There are a range of pension options available which can be found through the Civil Service website.

Selection ProcessIn accordance with the Civil Service Commission rules, the following list contains all selection criteria for the interview process.

Required ExperienceThis job advert encompasses a range of possible research and engineering roles within the Safeguard Analysis Team. The 'required' experiences listed below should be interpreted as examples of the expertise we're looking for, as opposed to a list of everything we expect to find in one applicant:

Writing production quality code

Writing code efficiently

Python

Frontier model architecture knowledge

Frontier model training knowledge

Model evaluations knowledge

AI safety research knowledge

Security research knowledge

Research problem selection

Research science

Written communication

Verbal communication

Teamwork

Interpersonal skills

Tackle challenging problems

Learn through coaching

#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 AI Jobs (With Real GitHub Examples)

In the fast-evolving world of artificial intelligence (AI), an impressive portfolio of projects can act as your passport to landing a sought-after role. Even if you’ve aced interviews in the past, employers in AI and machine learning (ML) are increasingly asking candidates to demonstrate hands-on experience through the projects they’ve built and shared online. This is because practical ability often speaks volumes about your suitability for a role—far more than any exam or certification alone could. In this article, we’ll explore how to build an outstanding AI portfolio that catches the eye of recruiters and hiring managers, including: Why an AI portfolio is crucial for job seekers. How to choose AI projects that align with your target roles. Specific project ideas and real GitHub examples to help you stand out. Best practices for showcasing your work, from writing clear READMEs to using Jupyter notebooks effectively. Tips on structuring your GitHub so that employers can instantly see your value. Moreover, we’ll discuss how you can use your portfolio to connect with top employers in AI, with a handy link to our CV-upload page on Artificial Intelligence Jobs for when you’re ready to apply. By the end, you’ll have a clear roadmap to building a portfolio that will help secure interviews—and the AI job—of your dreams.

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

In today's competitive AI job market, nailing a technical interview can be the difference between landing your dream role and getting lost in the crowd. Whether you're looking to break into machine learning, deep learning, NLP (Natural Language Processing), or data science, your problem-solving skills and system design expertise are certain to be put to the test. AI‑related job interviews typically involve a range of coding challenges, algorithmic puzzles, and system design questions. You’ll often be asked to delve into the principles of machine learning pipelines, discuss how to optimise large-scale systems, and demonstrate your coding proficiency in languages like Python, C++, or Java. Adequate preparation not only boosts your confidence but also reduces the likelihood of fumbling through unfamiliar territory. If you’re actively seeking positions at major tech companies or innovative AI start-ups, then check out www.artificialintelligencejobs.co.uk for some of the latest vacancies in the UK. Meanwhile, this blog post will guide you through 30 real coding & system-design questions you’re likely to encounter during your AI job interview. This list is designed to help you practise, anticipate typical question patterns, and stay ahead of the competition. By reading through each question and thinking about the possible approaches, you’ll sharpen your problem-solving skills, time management, and critical thinking. Each question covers fundamental concepts that employers regularly test, ensuring you’re well-equipped for success. Let’s dive right in.

Negotiating Your AI Job Offer: Equity, Bonuses & Perks Explained

Artificial intelligence (AI) has proven itself to be one of the most transformative forces in today’s business world. From smart chatbots in customer service to predictive analytics in finance, AI technologies are reshaping how organisations operate and innovate. As the demand for AI professionals grows, so does the complexity of compensation packages. If you’re a mid‑senior AI professional, you’ve likely seen job offers that include far more than just a base salary—think equity, bonuses, and a range of perks designed to entice you into joining or staying with a company. For many, the focus remains squarely on salary. While that’s understandable—after all, your monthly take‑home pay is what covers day-to-day expenses—limiting your negotiations to salary alone can leave considerable value on the table. From stock options in ambitious startups to sign‑on bonuses that ‘buy you out’ of your current contract, modern AI job offers often include elements that can significantly boost your long-term wealth and job satisfaction. This article aims to shed light on the full scope of AI compensation—specifically focusing on how equity, bonuses, and perks can enhance (or sometimes detract from) the overall value of your package. We’ll delve into how these elements work in practice, what to watch out for, and how to navigate the negotiation process effectively. Our goal is to provide mid‑senior AI professionals with the insights and tools to land a holistic compensation deal that accurately reflects their technical expertise, leadership potential, and strategic importance in this fast-moving field. Whether you’re eyeing a leadership role in machine learning at an established tech giant, or you’re considering a pioneering position at a disruptive AI startup, the knowledge in this guide will help you weigh the merits of base salary alongside the potential riches—and risks—of equity, bonuses, and other benefits. By the end, you’ll have a clearer sense of how to align your compensation with both your immediate lifestyle needs and long-term career aspirations.