Senior AI Engineer, Post-Training

Carta•San Francisco, CA

1d•$242,250 - $285,000

About The Position

Carta is seeking a Senior AI Engineer, Post-Training to join their ML Engineering team, embedded within Carta Law. This role focuses on leading technically complex, model-centric projects and serving as a multiplier for the team. The engineer will 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. The position involves working closely with engineers building the product and bringing AI capabilities to users within Carta's legal tech platform, which is built around autonomous AI agents, specialized legal models, document intelligence, and contract workflows.

Requirements

Hands-on experience with LLM post-training using PyTorch or equivalent frameworks.
Understanding of the training, evaluation, and inference systems around LLMs.
Comfortable building the product around the model, including agents, tools, services, and production infrastructure.
Ability to work across model and product engineering problems.
Keeps current on open-weight models and post-training techniques.
Owned model development or post-training work in applied settings.
Experience building AI systems around models that shipped to real users.
Ability to turn ambiguous product or model problems into tractable technical work.
Ability to make pragmatic trade-offs across research and engineering.
Ability to drive projects from idea through production with minimal guidance.
Strong judgment on model selection, data, training objectives, and evaluation.
Knowledge of when training is the right lever versus improving the agent, tools, context, or broader product.
Ability to make and defend decisions with data.
Ability to communicate decisions clearly across technical and domain teams.
Meaningful ownership of models or systems built in AI, applied research, or adjacent engineering roles.
Experience spans both model-level training work and the product and engineering systems around it, from shaping the technical approach through putting it into production.

Responsibilities

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.
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.
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 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.
Build and operate the systems that take models into production, including model serving, agents, evaluation pipelines, and the surrounding tooling and infrastructure.
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.
Work directly with lawyers and other domain experts to translate real workflows into model, data, and evaluation decisions.