Engineering Fellowship

10a Labs•San Francisco, CA

5h•Remote

About The Position

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 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: 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.

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 Haves

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)
Experience managing full lifecycle projects from design to deployment
Experience designing and building end-to-end backend systems, from architecture and data modeling to deployment, scaling, and monitoring
Proficiency in backend programming languages such as Python, Java, Kotlin, Node.js, or Go and experience building secure systems, APIs, and microservices
Knowledge of security best practices, including authentication methods (OAuth, JWT), encryption, and secure API development; knowledge of common attack vectors (SQL injection, privilege escalation, DDoS) and effective mitigation strategies
Experience with web scraping/crawling (e.g., Beautiful Soup, Selenium, Scrapy)
Computer vision skills (OCR, image classification, deep fake detection)
Familiarity with multimodal learning (text-image or text-audio) or cross-domain model evaluation
Exposure to MLOps tools and practices (Docker, Kubernetes, CI/CD for ML, MLflow, etc.)
Understanding of modern retrieval-augmented generation (RAG), AI agent frameworks, and context-aware orchestration (e.g., LangChain, LlamaIndex, OpenAI Agents, or AutoGen) for building intelligent applications

Responsibilities

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
Support other critical initiatives
Implementing cloud infrastructure for deploying machine learning models
Writing high-coverage test suites for complex codebases
Guiding project development with software engineering best practices, including version control, continuous integration, and design patterns
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
Developing front-end dashboards and other visualizations
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