Member of Technical Staff, Infrastructure
Harvey · New York
About this role
## Member of Technical Staff, Infrastructure — Harvey Research ### Why Harvey At Harvey, we’re transforming how legal and professional services operate by combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise. This is a rare opportunity to help build a generational company at a true inflection point—strong product-market fit, world-class investor support, and rapid scaling. We move fast, take ownership, and stay close to customers. Our values: **Decisiveness**, **Simplicity**, and **Job’s Not Finished**. ### Role Overview As a **Member of Technical Staff, Infrastructure** in Harvey Research, you’ll build the systems that power how we adapt and improve leading open-weight models for legal work. We recently post-trained our first legal-work model and are now building the team to scale up our post-training efforts. You’ll own the infrastructure behind that effort, including: - Distributed training - RL rollout systems - Training environments where models learn to use tools and complete complex legal workflows - Internal systems to manage data, run evals, and launch/track training jobs You’ll help decide what we build, what we adopt, and how the pieces fit together—joining a small, high-bar team where each person owns a large part of the stack. ### What You’ll Do - Scale our post-training stack, supporting **SFT** and **RL** on leading open-weight models - Build distributed rollout infrastructure coordinating model inference, tool execution, and training experience collection for long-horizon tasks - Create infrastructure for **reproducible agent environments** where models interact with documents, tools, and external systems during training and evaluation - Own performance and reliability of training runs (GPU utilization, checkpointing, failure recovery, observability) - Identify and remove bottlenecks across training and inference to shorten the path from research idea to useful result - Build internal tools/services: training data management & versioning, running evals & browsing results, launching and tracking training jobs/experiments - Integrate models into production (serving, latency, cost, and online evaluation) so research reaches customers quickly and safely - Partner closely with researchers to turn experimental workflows into dependable systems while preserving room for rapid iteration ### Learn More - https://harvey.ai/research
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