Research Scientist, Applied White-Box Methods

FAR.AI

. Take ownership of and accelerate the team's research agenda .

Posted 9/28/2026full-timeBerkeley • California • United StatesMid-LevelSenior💰 $150,000 - $250,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in AI safety through hands-on experience with white-box methods and a strong research background in machine learning or related fields. Capable of effectively communicating complex methods and findings to diverse audiences while collaborating with leading institutions in the AI alignment community.

Highest-signal resume keywords

White-Box Method ApplicationAI Safety ResearchEvaluations and AI ControlPhD in Computer Science or Related FieldApplied Machine Learning Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

White-Box MethodsActivation ExplainersReinforcement LearningTraining-Dynamics ShapingReward HackingModel InternalsEvaluation AwarenessRed-TeamingLarge Training RunsGoodhart-Resistant Metrics

Soft Skills

Clear CommunicationCollaboration

Tools & Technologies

FAR.AI ComputeAI Alignment Community Engagement

Industry Keywords

AI SafetyMachine LearningPhysicsStatisticsBiologyChemistryMaterials ScienceRobotics

About the role

Key responsibilities & impact

Take ownership of and accelerate the team's research agenda
Propose new research directions within the team's agenda
Develop, evaluate, and demonstrate methods leveraging model internals to improve AI safety
Stress-test white-box methods on real-world tasks, including long-context agentic coding
Evaluate methods against strong black-box methods and activation probes
Study applications including white-box control, evaluation awareness, training-dynamics shaping, reward hacking, sandbagging, and research tampering
Define realistic evaluations with Goodhart-resistant metrics
Develop and iterate methods using FAR.AI's compute and infrastructure
Ensure methods are simple and efficient enough for frontier-scale deployment
Publish findings broadly and engage with the AI alignment community
Attend relevant conferences and community events
Collaborate with national AI safety institutes, frontier model developers, and top academics

Requirements

What you’ll need

Hands-on experience applying at least one white-box method to a real model (activation explainers, SAEs, steering, attribution, influence functions, probes, or similar), and an informed view of its limitations
A track record in AI safety: a paper, a fellowship project, or substantive public writing
Experience with evaluations, AI control, red-teaming, reinforcement learning, or post-training of LLMs
Ability to communicate novel methods and results clearly to technical and non-technical audiences
A PhD or several years of research experience in computer science, machine learning, physics, statistics, or a related field
Experience in applied ML for fields such as biology, chemistry, materials science, or robotics
Prior hands-on experience with white-box methods and demonstrated interest in their practical application preferred
Experience running large training runs is a plus
Willingness to work on-site from the Berkeley, CA office at least 3 days per week for applicable arrangements

Benefits

Comp & perks

Health Insurance - 94% of Insurance premium paid by Organization commencing within 1 month after your start date
Retirement - 401(k) plan with up to 2% match
PTO - 25 days Paid Time Off per year, accrued weekly and up to 10 days of paid sick leave per year
Paid Leave - Paid Bereavement, Family, Medical and Pregnancy Disability Leave
WFH Stipend & Equipment - Work computer and stipend provided for eligible employees
Catered Meals (Berkeley Office Only) - Catered lunches and dinners on workdays at our office
Work-related travel and equipment expenses
Catered lunch and dinner at the Berkeley office
Visa sponsorship for in-person employees