Senior AI Engineer, Post-Training

<div class="content-intro"><h2>The Company You’ll Join</h2> <p>Carta is the connected platform and AI-native ecosystem for private capital. Built to replace fragmented tools with a single system of record, Carta brings together the software, services, and legal infrastructure that founders use to manage equity, fund managers use to run administration and reporting, and legal teams use to close transactions. Trusted by 55,000 companies and 1.8M+ equity holders in 160+ countries, and 10,000 funds and SPVs representing $250B+ in assets under management, Carta is transforming how private capital operates. Recognized by Fortune, Forbes, Fast Company, Inc. and Great Places to Work.</p> <p>For more information about our offices and culture, check out our <a href="https://carta.com/careers/">Carta careers page</a>.</p></div><h2><strong>The Team You'll Work With</strong></h2> <p>You’ll join Carta’s ML Engineering team, embedded in Carta Law, our legal tech platform built around autonomous AI agents, specialized legal models, document intelligence and contract workflows. You’ll have end-to-end ownership across model development and applied AI, from post-training and evaluation through model serving and the agents and systems built around those models. You'll work closely with the engineers building the product and bringing these capabilities to users.</p> <h2><strong>The Problems You'll Solve</strong></h2> <p>As an AI Engineer, you will lead technically complex, model-centric projects and serve as a multiplier for your team. You will:</p> <ul> <li>Post-train open-weight language models on proprietary legal data, owning the model development lifecycle end-to-end, from data, objective design, and base-model selection through training, evaluation, and iteration.</li> <li>Apply the right training techniques for the problem, including supervised fine-tuning, preference optimization, reinforcement learning, and related methods, with careful attention to reward and grader design, model behavior, and evaluation.</li> <li>Build and improve training datasets and data pipelines, including labeling guidance, model-generated data, and human feedback loops with domain experts.</li> <li>Own the training stack needed to run experiments reliably, using managed or self-hosted infrastructure as appropriate, and understand distributed training well enough to diagnose and optimize training runs.</li> <li>Build and operate the systems that take models into production, including model serving, agents, evaluation pipelines, and the surrounding tooling and infrastructure.</li> <li>Partner with product and agent engineers on model/system co-design, deciding what belongs in the model versus the agent harness, tools, context, and workflow.</li> &