Job description

The Company You’ll Join 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. For more information about our offices and culture, check out our Carta careers page . The Team You'll Work With 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. The Problems You'll Solve As an AI Engineer, you will lead technically complex, model-centric projects and serve as a multiplier for your team. You will: 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. About You Technical Depth: You have hands-on experience with LLM post-training using PyTorch or equivalent frameworks, and understand the training, evaluation, and inference systems around them. You are equally comfortable building the product around the model, including agents, tools, services, and production infrastructure. You can work across model and product engineering problems as needed. You stay current on open-weight models and post-training techniques. Execution: You have owned model development or post-training work in applied settings and built AI systems around those models that shipped to real users. You can turn ambiguous product or model problems into tractable technical work, make pragmatic trade-offs across research and engineering, and drive projects from idea through production with minimal guidance. Strategic Mindset: You have strong judgment on model selection, data, training objectives, and evaluation, and know when training is the right lever versus improving the agent, tools, context, or broader product. You can make and defend those decisions with data and communicate them clearly across technical and domain teams. Experience: You have done ambitious work in AI, applied research, or adjacent engineering roles, with meaningful ownership of the models or systems you built. You can point to work that materially improved model capability or product outcomes. Your 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. At Carta, you’re not just an employee. You’re a builder who is creating infrastructure that accelerates innovation and empowers more ownership. Cartans are helpful, relentless, unconventional and kind; representing Carta’s Identity Traits. They work collaboratively and cross functionally to challenge the status quo; working towards a common goal of creating more owners in the private markets. Salary Carta’s compensation package includes a market competitive salary, equity for all full time roles, exceptional benefits, and, for applicable roles, commissions plans. Our minimum cash compensation (salary + commission if applicable) range for this role is: $242,250 - $285,000 in San Francisco, CA and in New York City, New York Final offers may vary from the amount listed based on geography, candidate experience and expertise, and other factors. Disclosures: We are an equal opportunity employer and are committed to providing a positive interview experience for every candidate. If accommodations due to a disability or medical condition are needed, please connect with the talent partner via email. Carta uses E-Verify in the United States for employment authorization. See the E-Verify and Department of Justice websites for more details. For information on our data privacy policies, see Privacy , CA Candidate Privacy , and Brazil Transparency Report . Please note that all official communications from us will come from an @carta.com or @carta-external.com domain. Report any contact from unapproved domains to security@carta.com.

Requirements

Must-haves

The Company You’ll Join
For more information about our offices and culture, check out our Carta careers page . The Team You'll Work With
The Problems You'll Solve
As an AI Engineer, you will lead technically complex, model-centric projects and serve as a multiplier for your team. You will:
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.
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.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlllm

Platform: greenhouse
Effort: quick