Engineering Fellowship

10a Labs provides safety and threat-intelligence services for AI labs, enterprises, and technology platforms through adversarial red teaming, model evaluations, and intelligence collection. The Engineering Fellow will support applied research and engineering projects involving data processing, visualization, machine learning model deployment, and cloud infrastructure, with specialization in Software Engineering, Data Engineering, or Machine Learning.

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; and
• Support other critical initiatives
• 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
• 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
• 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

Requirements:

• 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
• 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
• 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