Artificial Intelligence Researcher

The unnamed client is an established AI safety and security business. The Artificial Intelligence Researcher will define and deliver benchmarks for measuring frontier AI risks, owning their taxonomies, evaluation harnesses, quality standards, and releases. The role also coordinates contributors, shapes the research release roadmap, and maintains relationships across the AI research ecosystem.

Responsibilities

• Delivering on a fast release rhythm. Expect to put out a new benchmark roughly every fortnight to three weeks. Scope flexes with the topic: a conversational taxonomy might hold around a hundred evals, whereas an agentic or RL-based benchmark may be closer to twenty, given how costly each item is to review. How sensitive the material is will determine whether it goes public or stays with the labs
• Holding the standard. Success looks like this: a frontier lab re-runs your benchmark and reproduces your results. Verifiers are robust, rubrics are unambiguous, the data distribution makes sense, and the lab's own domain experts see the taxonomy as genuinely new. Model-assisted screening is fine, but you'll still go through the evals personally, catch anything that drifts from the taxonomy, and send it back when it isn't right
• Keeping delivery on track. You own the plan and the schedule, and you're the one making sure contributing researchers hit their dates. When needed, you'll bring in and steer a small number of freelance experts directly
• Shaping the roadmap. Around once a month, you'll join the CTO alongside pod and research leads to review what the research teams are seeing, what customers want, and what's happening across the wider landscape. The outcome is an updated quarterly release plan, aligned with the commercial relationships the business is looking to build
• Staying plugged into the field. Roughly a fifth of your time is set aside for the wider ecosystem: keeping up with new research, maintaining relationships with people inside the labs, understanding what's worrying them, and attending a handful of conferences each year. Regular, ideally weekly, conversations with lab contacts are part of the job

Preferred

• Post-training experience across SFT, DPO or GRPO, particularly reward design for subjective or safety-critical objectives
• Experience evaluating agentic systems, including tool use, orchestration, permissioning and prompt injection
• Papers accepted at leading ML or security venues
• Confidence presenting your research to customers and to large or senior audiences
• Post-Training SFT
• Post-Training DPO
• Post-Training GRPO
• Reward Design
• Subjective Objective Optimization
• Safety-Critical Objective Optimization
• Agentic Systems Evaluation
• System Orchestration
• Permissioning
• Prompt Injection
• ML Research Publications
• Security Research Publications
• Research Presentation
• Customer Communication
• Executive Communication

Company

• Executive search recruitment for senior management and executives
• Singapore, Central Region, SGP
• https://www.space-exec.com/
• Founded in 2015
• 11-50 employees

Qualifications

• A Masters or PhD in computer science, machine learning or a similar discipline, or comparable depth gained through industry research
• At least three years designing and operating safety or security evaluations for LLMs in production, whether at a frontier lab, a model provider or a specialist safety/security research group
• A track record of publishing in AI safety and security, with five or more relevant papers and first authorship on at least two
• Solid engineering skills: building eval harnesses, distributed inference, vLLM, and the ability to dig into an unfamiliar codebase and fix it
• The ability to design a taxonomy from scratch, not just evaluate against an existing one
• Comfort leading a researcher and a couple of freelancers through influence rather than formal line management
• Excellent written and verbal English
• Genuine curiosity about the harms being studied, and an appetite for getting up to speed on a new domain every few weeks
• Post-training experience across SFT, DPO or GRPO, particularly reward design for subjective or safety-critical objectives
• Experience evaluating agentic systems, including tool use, orchestration, permissioning and prompt injection
• Papers accepted at leading ML or security venues
• Confidence presenting your research to customers and to large or senior audiences
• At least three years designing and operating safety or security evaluations for LLMs in production, whether at a frontier lab, a model provider or a specialist safety/security research group.

Required skills

• AI Safety and Security Research, Agentic Systems Evaluation, Distributed Inference, LLM Safety Evaluations, LLM Security Evaluations, Post-training DPO, Post-training GRPO, Post-training SFT, Prompt Injection, Reward Design, Taxonomy Design, VLLM

Preferred skills

• AI Tools, Analyze How Well You Fit, Build Cover Letter, Company data provided by, Customize Your Resume, Make your application stand out, Maximize your interview chances, Papers accepted at leading ML or security venues, Singapore, Central Region, SGP, Understand your strength & weakness, https://www.space-exec.com/

Experience

• 3+ years