Applied Data Scientist

About the role: We’re looking for an applied data scientist with strong engineering instincts, deep analytical thinking and excellent technical execution. You’ll develop evaluation frameworks, design and automate red-teaming strategies, own quality metrics, and run adversarial testing initiatives to support short-term sprints and long-term initiatives aligned with AI safety goals. You’ll also coordinate with red-teamers, ML engineers, and infrastructure teams to ensure end-to-end product readiness and robustness.

In this role, you will:

• Design the technical implementation of a robust red teaming project.
• Lead adversarial testing efforts (e.g., red teaming, evasion probes, jailbreak simulation) and analysis efforts.
• Work with researchers and domain experts to define labeling schemas and edge-case tests.
• Partner with ML and infrastructure engineers to ensure production readiness, observability, and performance targets.
• Communicate technical strategy and tradeoffs clearly across internal and client teams.
• Automate red teaming, including developing automated workflows for prompt generation, model evaluation, and execution of AI experiments; fine-tune LLMs or classification systems. 
• Brainstorm novel research approaches to both known and emerging problems involving AI, data, and the internet. 

We’re looking for someone who:

• Has 3-5 years of experience in applied data science, ML product work, or security-focused AI, including technical leadership or staff-level ownership.
• Has designed and evaluated real-world ML systems with a focus on model behavior, error analysis, and continuous improvement.
• Can design red teaming workflows to surface model blind spots and failure modes.
• Operates effectively across ML, infra, and policy / strategy contexts.
• Thinks like a builder, analyst, and communicator all in one.

Requirements:

• Degree (or equivalent work experience) in Data Science, Information Science, Computer Science with ML focus, or a related field (graduate degree preferred). 
• Background in data science, applied ML, or ML engineering, with proven experience in production-grade systems.
• Strong analytical toolkit (Python, SQL, Jupyter, scikit-learn, Pandas, etc.) and familiarity with modern ML tooling (e.g., PyTorch, Hugging Face, LangChain).
• Experience working with LLMs and embedding-based classification systems.
• Excellent communication skills across strategy and technical domains.
• Comfort working in fast-moving, high-impact environments, such as startups, AI research labs, or security-focused teams.

Nice to have experience with:

• Safety evaluation, red teaming, or adversarial content testing in LLMs. 
• Trust & safety or risk-focused classification systems.
• Annotation ops, feedback loops, or evaluation pipeline design.
• Experience with open-source model evaluation tools (Promptfoo, DeepEval, etc.).

Compensation & Benefits:

• Salary Range: $105K–$125K, depending on experience and location.
• Bonus: Performance-based annual bonus.
• Professional Development: Support for conferences, continuing education, or leadership training.
• Work Environment: Fully remote, U.S.-based.
• Health Benefits: Comprehensive health, dental, and vision coverage.
• Time Off: Generous PTO and paid holiday schedule.
• Retirement: 401(k) plan.