Engineering Fellowship

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)
• Experience managing full lifecycle projects from design to deployment

Software Engineering

• 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

Data Engineering

• Experience with web scraping/crawling (e.g., Beautiful Soup, Selenium, Scrapy)

Machine Learning Engineering

• 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

Benefits

• Flexible start / end dates
• Remote work (based in the continental U.S.)
• Flexible schedule, up to 20 hours per week (negotiable)
• Hourly pay commensurate with experience and qualifications
• $30 per hour for undergraduate students
• $35 per hour for graduate students
• $50 per hour for advanced PhD students
• $60 per hour for postdocs or non-tenured positions
• $125 per hour for tenure-track academics