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Senior Member of Technical Staff - Model Safety
Accepting applicationsXcede · San Francisco Bay Area
Full-Time Mid_senior AIMachine Learningaiate
Posted
1d ago
Category
Test
Experience
Mid_senior
Country
United States
Member of Technical Staff – AI Safety
📍 San Francisco | New York | London (On-Site)
We're partnering with a frontier AI research company on a search for a Member of Technical Staff focused on AI Safety.
The company is building next-generation open-weight foundation models with a mission to make advanced AI broadly accessible. Their team includes researchers, engineers, and operators from some of the world's leading AI labs and technology companies, working on the frontier of model capabilities, alignment, and deployment.
This is an opportunity to help define how advanced AI systems are evaluated, stress-tested, and safely deployed. You'll be working at the intersection of adversarial research, red teaming, model evaluation, alignment, and software engineering—helping ensure next-generation foundation models are robust, reliable, and secure before they reach the world.
Overview:
Lead red-teaming and adversarial evaluation efforts to uncover model vulnerabilities, misuse risks, and alignment gaps
Design and build scalable safety evaluation frameworks and automated testing pipelines
Partner closely with alignment researchers to translate safety findings into production guardrails
Act as a key decision-maker in determining whether model releases meet safety thresholds
Research and implement state-of-the-art jailbreaking techniques and defenses to stay ahead of emerging threats
Develop dynamic safety benchmarks that evolve alongside increasingly capable AI systems
Skills required:
Deep expertise in LLM safety, red teaming, adversarial attacks, interpretability, or model evaluation
Strong software engineering skills and experience building large-scale ML systems or automated evaluation infrastructure
Experience with AI alignment techniques, RLHF, RLAIF, or related safety methodologies is highly desirable
MS, PhD, or equivalent practical experience in Machine Learning, AI Safety, Computer Science, or a related field
Ability to operate in a high-agency, fast-moving environment and make high-stakes technical decisions
Show more Show less
📍 San Francisco | New York | London (On-Site)
We're partnering with a frontier AI research company on a search for a Member of Technical Staff focused on AI Safety.
The company is building next-generation open-weight foundation models with a mission to make advanced AI broadly accessible. Their team includes researchers, engineers, and operators from some of the world's leading AI labs and technology companies, working on the frontier of model capabilities, alignment, and deployment.
This is an opportunity to help define how advanced AI systems are evaluated, stress-tested, and safely deployed. You'll be working at the intersection of adversarial research, red teaming, model evaluation, alignment, and software engineering—helping ensure next-generation foundation models are robust, reliable, and secure before they reach the world.
Overview:
Lead red-teaming and adversarial evaluation efforts to uncover model vulnerabilities, misuse risks, and alignment gaps
Design and build scalable safety evaluation frameworks and automated testing pipelines
Partner closely with alignment researchers to translate safety findings into production guardrails
Act as a key decision-maker in determining whether model releases meet safety thresholds
Research and implement state-of-the-art jailbreaking techniques and defenses to stay ahead of emerging threats
Develop dynamic safety benchmarks that evolve alongside increasingly capable AI systems
Skills required:
Deep expertise in LLM safety, red teaming, adversarial attacks, interpretability, or model evaluation
Strong software engineering skills and experience building large-scale ML systems or automated evaluation infrastructure
Experience with AI alignment techniques, RLHF, RLAIF, or related safety methodologies is highly desirable
MS, PhD, or equivalent practical experience in Machine Learning, AI Safety, Computer Science, or a related field
Ability to operate in a high-agency, fast-moving environment and make high-stakes technical decisions
Show more Show less