Applied Safety Research Engineer, Safeguards

Anthropics Technology Ltd•New York, NY

9d•$320,000 - $405,000•Hybrid

About The Position

Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. We are looking for a research-oriented engineer to develop the methods that make our safety evaluations representative, robust, and informative. You'll work on questions like: How do we measure whether a model is safe? How do we create evaluations that reflect real-world usage rather than synthetic benchmarks? How do we know our graders are accurate? This role sits at the intersection of applied ML research and engineering. You'll design experiments to improve how we evaluate model behavior, then ship those methods into pipelines that inform model training and deployment decisions. Your work will directly shape how Anthropic understands and improves the safety of our models across misuse, prompt injection, and user well-being.

Requirements

Have 4+ years of software engineering or ML engineering experience
Are proficient in Python and comfortable working across the stack
Have experience building and maintaining data pipelines
Are comfortable with data analysis and can draw insights from large datasets
Have experience with LLMs and understand their capabilities and failure modes
Can move fluidly between prototyping and production-quality code
Are excited by ambiguous problems and can translate them into concrete experiments
Care deeply about AI safety and want your work to have real impact

Nice To Haves

Red teaming, adversarial testing, or jailbreak research on AI systems
Building or contributing to LLM evaluation frameworks or benchmarks
Trust and safety, content moderation, or abuse detection systems
Synthetic data generation or data augmentation
Distributed systems or large-scale data processing
Prompt engineering or LLM application development

Responsibilities

Design and run experiments to improve evaluation quality—developing methods to generate representative test data, simulate realistic user behavior, and validate grading accuracy
Research how different factors (multi-turn conversations, tools, long context, user diversity) impact model safety behavior
Analyze evaluation coverage to identify gaps and inform where we need better measurement
Productionize successful research into evaluation pipelines that run during model training, launch and beyond.
Collaborate with Policy and Enforcement to translate real-world harm patterns into measurable evaluations
Build tooling that enables policy experts to create and iterate on evaluations
Surface findings to research and training teams to drive upstream model improvements