• Assess business unit workflows and identify where automation, agents, or custom tooling create real leverage • Design and build agents for internal operations: orchestration, tool-calling patterns, escalation paths, and human-in-the-loop gates • Build custom internal tools, integrations, and infrastructure that support business operations across functions • Evaluate enterprise data systems for AI readiness: quality, accessibility, governance, and latency. Build the access patterns that make enterprise data usable by AI systems. • Define the AI application lifecycle for internally-built systems: how artifacts get verified, tested, deployed, and maintained. Establish quality gates between prototype and production. • Define and enforce AI governance practices: agent permissions, escalation paths, decision audit trails, behavioral monitoring, and the boundary between autonomous action and human oversight • Build and operate internal AI infrastructure: self-service capabilities, deployment guardrails, observability, and cost management • Build tools and interfaces that make AI capabilities accessible to non-technical teams, translating business problems into working systems they can use directly

• Implement new AI features in products • Build tools & frameworks augmenting the capabilities of LLM/VLMs • Build AI automations & AI agents • Fine-tune AI models • Fine-tune and maintain models with the HuggingFace toolkit • Monitor model's performance • Work with and guide internal teams on building custom datasets • Rapidly build and test AI-driven concepts — moving from proof of concept → MVP → production. • Evaluate existing solutions (both internal & external) and conduct experiments on potential improvements

• Lead and execute the vision, strategy, and roadmap for Reddit’s large-scale GenAI Platform. • Define the platform architecture and operating model that enable teams to build, deploy, and scale GenAI products reliably. • Drive the strategy for a unified LAG Gateway supporting internally and externally hosted LLMs through consistent APIs and abstractions. • Set the direction for core platform capabilities such as rate and token limit management, intelligent failover, and production resilience. • Shape Reddit’s approach to an enterprise-grade RAG system • Establish the strategic direction for agentic AI workflows and tool-use patterns across the platform. • Own the end-to-end platform strategy from concept through production adoption and long-term evolution. • Drive MLOps and LLMOps standards across CI/CD, testing, versioning, evaluation, and lifecycle management. • Define best practices for observability, monitoring, governance, and operational excellence across GenAI systems. • Partner across engineering, product, and leadership to align platform investments with company priorities and user needs. • Champion platform thinking with a strong focus on scalability, reliability, performance, and developer experience. • Influence technical direction across teams by turning emerging AI capabilities into a scalable platform strategy.

• Drive GenAI Infrastructure Strategy: Propose, design, and lead the architecture of our next-generation LLM platform, significantly advancing our capabilities to support large-scale foundation models that serve millions of redditors. • Design Resilient, Large-Scale Distributed Systems: Architect highly fault-tolerant training infrastructure capable of supporting multi-week, distributed workloads across massive GPU clusters. You will tackle challenges related to automated recovery, cluster-scale health monitoring, and advanced checkpointing to ensure optimal compute efficiency. • Build Self-Serve LLM Workflows: Design and implement robust, production-grade pipelines for LLM fine-tuning (e.g., SFT, RLHF/DPO). You will abstract away the complexity of distributed training frameworks, integrating them into a seamless platform SDK that handles configuration, experiment tracking, and model lifecycle management. • Develop Comprehensive Evaluation & Benchmarking Infrastructure: Treat model evaluation as a first-class platform capability. You will build scalable systems for automated regression detection, structured metrics tracking, and complex inference-heavy evaluation patterns to ensure the quality and safety of models before they hit production. • Architect Advanced Data Ingestion Pipelines: Extend our distributed data platforms to natively and efficiently handle the massive, multimodal datasets (text, image, video) required for modern GenAI workloads, optimizing for throughput and dynamic batching. • Provide Technical Leadership & Mentorship: Analyze complex bottlenecks in distributed systems to optimize for performance and cost-efficiency. Mentor senior engineers, champion a rigorous MLOps culture, and partner with cross-functional leadership to define technical roadmaps and de-risk major initiatives.