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.

Location & Travel Expectations

We are primarily seeking candidates based in the Washington, DC, New York City, San Francisco Bay Area, and Denver metropolitan areas. Candidates hired in these locations will be expected to work from a 10a Labs office at least two days per week.

All new hires will participate in in-person onboarding in Washington, DC, New York City, or San Francisco during their first week. Occasional travel may also be required for interviews, in-person meetings, and other business needs.

About the role: As an Engineering Fellow, you will apply your technical skills to support high-impact research problems. Fellows will contribute across the project lifecycle — from processing diverse data sources and designing dynamic visualizations, to deploying sophisticated models and building cloud infrastructure. This is a hands-on role at the intersection of applied research and practical engineering, with opportunities to explore novel methods, test ideas quickly, and generate insights.

Fellows specialize in one of three concentrations based on interest and past experience: Software Engineering, Data Engineering, or Machine Learning.

In this role, you will

Collaborate with engineers on real projects, including client-facing products and in-house tooling;
Assist with researching experiment design and automation, particularly as it relates to abuse detection or red teaming of AI systems;
Ideate / brainstorm new research approaches to known and novel problems in the Trust & Safety and AI Security fields; and
Support other critical initiatives.

Software Engineering concentration responsibilities may include

Implementing cloud infrastructure for deploying machine learning models;
Writing high-coverage test suites for complex codebases; and
Guiding project development with software engineering best practices, including version control, continuous integration, and design patterns.

Data Engineering concentration responsibilities may include

Sourcing, curating, and processing diverse data sources across domains and modalities, including automated collection of internet-scale datasets;
Designing data architecture schemata, implementing with production-grade data storage tools, and interfacing via custom APIs; and
Developing front-end dashboards and other visualizations.

Machine Learning concentration responsibilities may include

Training, validating, evaluating, and deploying cutting-edge machine learning algorithms including classifiers, LLMs, and computer vision models;
Building agentic systems for automated prompting, red-teaming, research, and rapid experimentation;
Supporting projects with specialized knowledge of frontier model architectures and cutting-edge technology.

We’re looking for someone who

Brings curiosity and creativity to ambiguous research problems, with a bias toward experimentation and rapid iteration;
Thrives in collaborative, interdisciplinary environments; is resourceful, proactive, and adaptable;
Is comfortable communicating technical ideas clearly to both technical and non-technical audiences; and
Is excited about contributing to real-world applications and exploring new methods that push beyond standard benchmarks.

Requirements

Strong academic background and quantitative foundation demonstrated through applied coursework, research, or hands-on-experience
Strong Python background
Clear communicator of technical concepts for non-technical audiences

Nice to have

Familiarity with Google Cloud Platform (or similar), including storage and database services (e.g., Cloud Storage, CloudSQL, Cloud Spanner), workflow orchestration (e.g., Cloud Composer/Airflow, Cloud Run, Pub/Sub), and ML services (e.g., Vertex AI, Compute Engine)