Carta Logo

Carta

Senior AI Engineer, Post-Training

Posted 24 Days Ago
Hybrid
San Francisco, CA, USA
242K-285K Annually
Senior level
Hybrid
San Francisco, CA, USA
242K-285K Annually
Senior level
Lead end-to-end post-training of open-weight language models on proprietary legal data, including dataset development, objective design, supervised fine-tuning, preference optimization, reinforcement learning, evaluation, and distributed training. Build production model-serving systems, agents, tooling, and infrastructure. Partner with product engineers, lawyers, and domain experts to translate workflows into model and evaluation strategies and deliver AI capabilities to users.
The summary above was generated by AI
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 [email protected].
HQ

Carta San Francisco, California, USA Office

333 Bush St, San Francisco, CA, United States, 94104

Carta Santa Clara, California, USA Office

2445 Augustine Dr., Santa Clara, CA, United States, 95054

Similar Jobs

2 Minutes Ago
In-Office
210K-300K Annually
Senior level
210K-300K Annually
Senior level
Artificial Intelligence • Fintech • Financial Services
Own and build Confido's ML platform: design and operate end-to-end ML pipelines, infrastructure-as-code and CI/CD, optimize training/inference and agent workloads for latency/cost/throughput, integrate model data flows with data engineering, and ensure reliability, observability, security, and production model quality with online evals and human-in-the-loop review.
Top Skills: AirflowAurora/RdsAWSBedrockBentomlGithub ActionsGpuJavaKafkaKubernetesMlflowOnnxPythonRayRedisRuby On RailsSagemakerSnowflakeTensorrtTerraformVector DatabasesVertex AiVllm
8 Minutes Ago
Remote or Hybrid
4 Locations
Mid level
Mid level
Financial Services
Coordinates group air travel programs from intake and setup through post-event follow-up. Serves as a liaison among clients, travel advisors, and internal teams; manages timelines, documentation, bookings, traveler activity, arrivals, departures, reports, quality control, and stakeholder updates. Requires three or more years of travel-industry experience, GDS or self-booking tool proficiency, Microsoft Office skills, strong communication, and multitasking abilities.
Top Skills: Global Distribution Systems (Gds)ExcelMicrosoft OutlookMicrosoft WordSelf-Booking Tools
Senior level
Financial Services
Leads Client360 product strategy, discovery, roadmap, backlog, metrics, and lifecycle execution. Drives business initiatives, process improvement, operational efficiency, stakeholder alignment, and cross-functional delivery across banking functions. Uses analytics, machine learning, and AI to generate insights, while guiding launches, managing risk, improving platforms, and mentoring junior team members.
Top Skills: Artificial IntelligenceCrm SystemsData AnalyticsData GovernanceMachine Learning

What you need to know about the San Francisco Tech Scene

San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.

Key Facts About San Francisco Tech

  • Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
  • Major Tech Employers: Google, Apple, Salesforce, Meta
  • Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
  • Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
  • Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
  • Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine

Sign up now Access later

Create Free Account

Please log in or sign up to report this job.

Create Free Account