Senior AI Engineer, Post-Training
Carta · San Francisco, CA; New York City, NY
About this role
## Senior AI Engineer, Post-Training ### The Company You’ll Join Carta is the connected platform and AI-native ecosystem for private capital—replacing fragmented tools with a single system of record. Carta brings together software, services, and legal infrastructure used by founders, fund managers, and legal teams to manage equity, run administration/reporting, and 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. For more on offices and culture: https://carta.com/careers/ ### The Team You’ll Work With You’ll join Carta’s **ML Engineering** team, embedded in **Carta Law**—a 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/systems** built around those models. ### The Problems You’ll Solve As an AI Engineer, you’ll lead technically complex, model-centric projects and act as a multiplier for your team. You will: - **Post-train open-weight language models** on proprietary legal data, owning the full model development lifecycle (data, objective design, base-model selection, training, evaluation, and iteration). - Apply the right **training techniques** (SFT, preference optimization, reinforcement learning, and related methods), with careful attention to **reward/grader design**, model behavior, and evaluation. - Build and improve **training datasets and data pipelines**, including labeling guidance, model-generated data, and human feedback loops with domain experts. - Own the **training stack** to run experiments reliably (managed or self-hosted infrastructure as appropriate) and diagnose/optimize distributed training runs. - Build and operate **production systems**: model serving, agents, evaluation pipelines, and supporting tooling/infrastructure. - Partner with product and agent engineers on **model/system co-design** (what belongs in the model vs. agent harness, tools, context, and workflow). - Work directly with **lawyers and domain experts** to translate real workflows into model, data, and evaluation decisions. ### About You - **Technical Depth:** Hands-on LLM post-training experience with **PyTorch (or equivalent)**; strong understanding of training/evaluation/inference systems; comfortable building product capabilities around models (agents, tools, services, production infrastructure). Stay current on open-weight models and post-training techniques. - **Execution:** Owned model development/post-training in applied settings; built AI systems that shipped to real users. Can turn ambiguous problems into tractable technical work, make pragmatic research/engineering trade-offs, and drive projects from idea to production with minimal guidance. - **Strategic Mindset:** Strong judgment on model selection, data, training objectives, and evaluation. Knows when to use training vs. improving agents/tools/context/product. Can defend decisions with data and communicate clearly across technical and domain teams. - **Experience:** Meaningful ownership of models or systems in ambitious AI/applied research/adjacent engineering roles. Can point to work that materially improved model capability and/or product outcomes across both model-level training and production systems. ##
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