At a Glance
- Tasks: Evaluate AI models for safety and quality across complex topics.
- Company: Join a leading AI organisation focused on safety and innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborate with top researchers in a dynamic and impactful environment.
- Why this job: Make a real impact on AI safety and help shape technology for millions.
- Qualifications: 5+ years in AI Safety or related fields; strong analytical skills required.
The predicted salary is between 59400 - 72600 £ per year.
We are seeking experienced AI Safety Practitioners to evaluate the safety, quality, and alignment of frontier AI models across complex, policy-sensitive, and ambiguous ("grey-area") topics. You will assess AI-generated responses, apply safety policies, and help improve model behaviour through structured evaluations and feedback.
Responsibilities
- Evaluate AI-generated responses for safety, factual accuracy, policy compliance, and overall quality.
- Review content involving misinformation, political persuasion, self-harm, violence, cyber, biosecurity, and other sensitive domains.
- Apply and refine evaluation rubrics for RLHF, SFT, and AI safety benchmarking.
- Identify unsafe outputs, hallucinations, reasoning failures, and policy violations.
- Provide structured feedback to improve model alignment and safety performance.
- Collaborate with AI researchers and safety teams on ongoing evaluation initiatives.
Required Qualifications
- Bachelor's degree or higher in Journalism, Communications, Psychology, Sociology, Public Policy, Law, Biology, Chemistry, Computer Science, or a related discipline.
- 5+ years of professional experience in AI Safety, Trust & Safety, journalism, public policy, scientific research, security, or a related field.
- Excellent written English, critical thinking, and analytical reasoning skills.
- Ability to consistently evaluate nuanced and policy-sensitive scenarios.
Preferred Qualifications
- Experience with AI Safety, RLHF, SFT, Trust & Safety, or AI evaluation.
- Familiarity with safety policies, content moderation, or evaluation rubric development.
- Experience reviewing complex, high-risk, or ambiguous content.
Why Join?
- Shape the safety and behaviour of frontier AI models used by millions worldwide.
- Work on challenging, real-world safety evaluations across nuanced and high-impact domains.
- Collaborate with leading AI researchers, engineers, and safety teams.
AI Safety Practitioner - Expert Evaluator employer: Obsidian
Obsidian is an exceptional employer located in the vibrant Greater London area, offering a dynamic work culture that fosters innovation and collaboration among experts in the field. Employees benefit from a fast-start program with opportunities for growth and extension, alongside a commitment to quality in AI model training that makes a meaningful impact in genomics. With a focus on professional development and a supportive environment, Obsidian is dedicated to empowering its team members to excel in their careers.
StudySmarter Expert Advice🤫
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We think you need these skills to ace AI Safety Practitioner - Expert Evaluator
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Obsidian. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Obsidian
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Obsidian!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
