Jobs Companies Carta Senior AI Engineer, Post-Training

Sobre esta vaga de Senior AI Engineer, Post-Training na Carta

Carta · Presencial · San Francisco, CA; New York City, NY

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 PrivacyCA 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 [email protected].
Pronto para se candidatar à Carta?
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Como este salário de AI Engineer se compara

Esta vaga paga $263,625/yrem linha com da faixa típica para vagas de AI Engineer.

$202,000 a mediana $252,000 $517,000

Faixa típica $206,250–$405,000/yr, com base em 25 vagas de AI Engineer comparáveis na JobsRadar (pagamento anualizado em USD). Ver insights salariais de AI Engineer →

Sobre a Carta

NOTICE: All currently open roles will be available on carta.com/careers or our Linkedin page only. 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 [email protected].

Our mission

Carta connects founders, investors, and limited partners through world-class software, purpose-built for everyone in venture capital, private equity and private credit. 

 

Carta’s fund administration platform supports nearly 7,000 funds and SPVs, representing  $150B in assets under administration in venture capital and private equity. Trusted by more than 40,000 companies, Carta also helps private businesses in over 160 countries manage their cap tables, valuations, taxes, equity programs, compensation, and more.

 

Together, Carta is creating the end-to-end ERP platform for private markets. Traditional ERP solutions don’t work for Private Funds. Private capital markets need a comprehensive software solution to replace outdated spreadsheets and fragmented service providers. Carta’s software for the Office of the Fund CFO does just that - it’s a new category of software to make private markets look more like public markets - a connected ERP for private capital. 

Our culture

Carta has been included on the Fortune Best Place to Work Financial Services and Insurance and Forbes World’s Best Cloud Companies lists. We’ve also been recognized by Built In's 2025 Best Places to Work List in San FranciscoSeattleNew York, and Washington D.C., and certified as a Great Place to Work.

 

For more information about our offices and culture, check out our Carta careers page. If you'd like to be part of Carta's Talent Community, follow this link.

 


For information on our data privacy policies, see PrivacyCA Candidate Privacy, and Brazil Transparency Report.

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