Engineering Fellowship

Washington D.c.mid

OtherLow-Code Developer

3 views0 saves0 applied

Apply Now

Quick Summary

Overview

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.

Requirements Summary

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…

Technical Tools

airflowdockergcpgojavajavascriptkotlinkuberneteslangchainopenaipythonseleniumsqlab-testingci-cddatabase-designmachine-learningmicroservicesoauthsecurity-best-practices

About the Role

~1 min read

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.

Responsibilities

~1 min read

→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.

Responsibilities

~1 min read

→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.

Responsibilities

~1 min read

→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.

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

~1 min read

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

~1 min read

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