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Salary: USD 242,250 - 285,000 / annual
Carta is a connected platform and AI-native ecosystem for private capital, serving 55,000 companies, 1.8M+ equity holders, and 10,000+ funds representing $250B+ in assets under management across 160+ countries. You will 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.
In this role, you 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. You will lead technically complex, model-centric projects and serve as a multiplier for your team.
Key responsibilities include:
- Post-train open-weight language models on proprietary legal data, owning the full model development lifecycle from data and objective design through training, evaluation, and iteration
- Apply appropriate training techniques including supervised fine-tuning, preference optimization, reinforcement learning, and related methods, with careful attention to reward 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, and understand distributed training well enough to diagnose and optimize runs
- Build and operate production systems including model serving, agents, evaluation pipelines, and 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 domain experts to translate real workflows into model, data, and evaluation decisions
You will work closely with engineers building the product and bringing these capabilities to users, collaborating across technical and domain teams.
REQUIREMENTS:
- Hands-on experience with LLM post-training using PyTorch or equivalent frameworks, with deep understanding of training, evaluation, and inference systems
- Equally comfortable building product around models, including agents, tools, services, and production infrastructure; ability to work across model and product engineering problems
- Current knowledge of open-weight models and post-training techniques
- Proven track record of owning model development or post-training work in applied settings and building AI systems that shipped to real users
- Ability to turn ambiguous product or model problems into tractable technical work, make pragmatic trade-offs between research and engineering, and drive projects from idea through production with minimal guidance
- Strong judgment on model selection, data, training objectives, and evaluation; ability to know when training is the right lever versus improving the agent, tools, context, or broader product
- Experience with ambitious work in AI, applied research, or adjacent engineering roles with meaningful ownership of models or systems built
- Demonstrated ability to materially improve model capability or product outcomes
- Experience spanning both model-level training work and the product and engineering systems around it, from shaping technical approach through production deployment
