Ariel Gil

AI Safety Researcher & Mechatronics Engineer

Technical Research Manager at Pivotal Research

About Me

I work on both Technical AI Safety and Governance, with a focus on AI Control and failure modes of automated alignment. In 2023, I co-founded the AI Standards Lab, a 501(c)(3) supporting EU standards and the recent Codes of Practice, funded at $1M+. The Lab was a key contributor to the final GPAI Code of Practice. I've since transitioned to an advisory/board role to focus on technical research, now as Technical Research Manager at Pivotal Research.

Originally a Mechatronics Engineer, I was an early team member of a semi-autonomous ophthalmic surgical robotics startup that raised $50M and grew to 50+ employees. Later, I worked in ML-based ultrasound diagnostics and laser eye-floater treatment. My governance work is informed by engineering risk management experience and technical AI safety research. Currently interested in making automated alignment go well (or pausing before it fails), as well as training new researchers in the AI Agent era.

In my free time, I mountain bike, rock climb, hike, and dance salsa (when not injured!). I also do occasional small design projects and 3D printing. You can see some of my previous engineering projects in my design portfolio, or below.

Previous AI Safety Projects

Discernment/Scalable Oversight (Paused)

I developed a technical research agenda (and also wrote a governance paper) testing the “discernment problem” - scalable oversight failures specifically when using AI systems to automate AI safety research. I think there’s a chance we will hit this bottleneck soon, and it might make iterative alignment strategies less likely to work. See my governance preprint and my Reflection Testing agenda on LessWrong. This project is currently paused.

Scheming Detection Environment (Sept 2025)

Empirical research project testing whether coding assistants will ‘benevolently deceive’ clients to avoid implementing insecure code. I tried to set up a somewhat realistic toy environment for iterating monitors, using a 3-turn conversation. Project Details on LessWrong

Selected Publications and Papers

Engineering Projects

Tire & Vehicle Dynamics Modeling

3-year research project involving 300+ hours of MATLAB data analysis of empirical tire data and vehicle dynamics modeling. Built car/racetrack models for performance optimization. See Github Project with more details and Code. This work was later used for in a “team strategy” research project working top-down to define design requirements based on competition performance targets. (see below)

University Racecar Engineering

Led a 35-person university racecar engineering team to 28th place out of 110 international teams in the ‘Formula SAE’ competition, with a target top-10 finish the following year (competition cancelled due to COVID). Responsible for vehicle dynamics modeling, tire data analysis, and overall system integration. Over 300 hours of MATLAB data analysis and research over 3 years. See published strategy/vehicle modeling work and Electric Racecar capstone thesis project

Offroad Racecar

Technical lead for offroad university racecar design team (Baja SAE), responsible for suspension, steering, and ergonomics. The 2018-2019 car featured unusual (perhaps innovative?) kinematics with 70/30 weight distribution, short wheelbase, and high ground clearance, as well as long uncut bent tubing for easier welding.

Laser Eye Floater Removal (Pulsemendica)

Mechanical design - developing the motion system for a laser-based ophthalmic treatment device. Conducted risk analysis and usability mockups for the precision laser targeting system to be used for a non-invasive eye floater removal surgery.

Medical Robotics Startup (Forsight Robotics)

Early member (#4) of a semi-autonomous ophthalmic surgical robotics startup that grew to 50+ employees and raised $50M within ~1 year. Worked on end effector, surgical tools, system integration, force-feedback joystick design, and ergonomics testing. Iterated Vision/tool-tip tracking requirements with CV team, and learned about challenges in ML risk management (even for simple non-closed loop tracking). Conducted usability studies comparing robotic vs manual surgery with surgeons.

ML-based Ultrasound Diagnostics

Developed and 3D Printed data collection fixtures, for a machine learning based ultrasound diagnostics (heart disease and pregnancy checks) startup. The fixtures were used in clinical validation studies at various hospitals.