• Define the technical vision and long-term roadmap for Feed Retrieval, aligning large-scale recommender-system investments with Reddit’s product, ecosystem, and business objectives. • Translate broad Feed Relevance goals into a focused team roadmap, making clear prioritization tradeoffs across model quality, inventory expansion, experimentation velocity, infrastructure cost, and operational reliability. • Coach and support the development of your team, constantly seeking opportunities to grow their skills and impact. • Oversee the design, development, and optimization of retrieval systems that source relevant, diverse, fresh, and high-quality candidates for personalized feed experiences. • Establish strong measurement, experimentation, and debugging practices so the team can understand retrieval quality, candidate coverage, source incrementality, and downstream impact. • Collaborate with ML platform, infrastructure, ranking, safety, and product teams to build scalable, low-latency retrieval systems that can support the next generation of AI-powered recommendations. • Maintain high standards for system performance, reliability, latency, cost efficiency, and responsible recommendation practices. • Work with cross-functional partners from across the company to identify key areas of opportunity, set expectations, and communicate your team’s work. • Partner with our incredible recruiting team to attract, interview, and hire diverse and talented machine learning engineers, growing a world-class team.

• Define the technical vision and long-term roadmap for Feed Ecosystems. • Coach and support the development of your team. • Work closely with product, design, data science, safety, community, ads, and platform partners. • Oversee the design, development, and optimization of ML systems. • Help define and operationalize signals for subjective and objective quality. • Collaborate with platform and infrastructure teams to build scalable AI-powered systems. • Maintain high standards for system performance, reliability, efficiency, and responsible AI practices. • Partner with our recruiting team to attract, interview, and hire diverse and talented machine learning engineers.

Role Description Have you earned your OSCP, OSEP/OSED/OSWE, and gained offensive experience before and/or since then? Are you excited at the opportunity to contribute to the growth and education for the current and next generation of cybersecurity professionals? If the idea of building lab environments to provide hands-on experience for individuals to learn and upskill, then this might be the right role for you! Duties and Responsibilities - Content design and build - Researches and identifies topical, relevant attack vectors suitable for inclusion in OffSec labs, exams, and/or learning content. - Design and build VMs and multi-host environments from vectors across Linux and Windows, including Active Directory and Cloud attack paths. - Designs realistic scenarios and environment narratives that make each attack path plausible and discoverable through enumeration. - Ensures the range of vectors in each environment is appropriate to its stated difficulty level and learning objectives. - Maintains variety across the catalogue so that content remains distinct as it rotates through active use. - Quality, integrity, and reproducibility - Review labs for unintended solution paths and confirms each is solvable by the intended route within its expected time frame. - Validates that machines are deterministic and reproducible in an isolated environment, including reliable reset and revert behaviour. - Deliberate selection and use of software and OS versions, monitoring deployed components so that content behaves consistently over its lifetime. - Documents the steps required to complete each machine, with difficulty assessed at a level of detail that supports competency mapping and certification review. - Collaboration - Coordinates with the relevant team(s) to deploy, update, and retire content. - Coordinates with testing to ensure full coverage of both newly created and updated content. - Coordinates with the Lab team Manager and Content Architect on vector selection and exploit implementation within courses and learning paths. - Coordinates and makes recommendations to keep content additions consistent across products. - Regularly communicates content additions, changes, and retirements to relevant stakeholders. - Senior scope - Acts as a technical reviewer for lab concepts and builds produced by other Vulnerable Machine Engineers and community contributors, providing structured, actionable feedback. - Support and mentor other engineers on build standards, documentation quality, and content review practice. - Contributes across OffSec's lab and exam portfolio as priorities require, rather than to a single product. - Automation and tooling - Creates and maintains automation that makes lab creation timely, consistent, and repeatable. - Evaluates new and emerging technologies to make recommendations on their introduction into the lab estate. - Creates and maintains documentation for every lab, to a standard that allows any team member to rebuild it from the documentation alone, without assistance. Qualifications - OSCP minimum, OSCE³ preferred (or at least one OffSec 300-level cert required). - A Bachelor’s Degree in Systems Engineering, Computer Science, Information Systems, and/or Information Assurance from an accredited institution/related specialized field, or equivalent practical experience. - Demonstrable Active Directory and Cloud attack-path experience. - Proficiency with infrastructure-as-code and configuration management and version control. - Scripting proficiency (Python, PowerShell, Bash). - At least five or more years of experience as a Systems Engineer or in an Info-Sec related position, with experience reviewing or approving others' technical security content. - Experience with Penetration Testing. - Experience with Bug Bounty Hunting. - Experience as a Systems Administrator with a variety of Operating Systems: - MS Windows - MS Windows Based Server Systems - POSIX Server Systems - Linux and Mac Desktop Environments - Strong written and verbal communication skills with an ability to present technical ideas clearly to both technical and non-technical audiences. Work Location and Hours This role is a full-time salaried position. It is a fully remote position. Work hours for this position are flexible and will be performed from a home office. Direct Reports This position has no direct reports. However, the expectations of this role are to provide technical leadership. EEO OffSec provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.

• Design and build user-facing ML features that harness LLMs and generative AI to unlock new product capabilities • Partner with product, design, and ML research to prototype and deliver high-impact, ML-powered experiences • Own the technical architecture and implementation strategy for applied ML systems - balancing latency, observability, and iteration speed • Build scalable services and APIs that bring model outputs to users in trustworthy and intuitive ways • Collaborate across platform, infra, and legal/compliance teams to ensure ML deployments meet standards for safety, fairness, and performance • Establish and evangelize best practices for prompt design, model evaluation, and experimentation across the org