Applied Data Scientist

About 10a Labs: 10a Labs is the safety and threat-intelligence layer trusted by frontier AI labs, AI unicorns, Fortune 10 companies, and leading global technology platforms. Our adversarial red teaming, model evaluations, and intelligence collection enable engineering, safety, and security teams to stay ahead of evolving threats and deploy AI systems safely.

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.