Researcher, Post Training

Our mission is to architect AI that learns from and interacts with the world like humans do.

We're pioneering the model architectures that will make this possible. Our founding team met as PhDs at the Stanford AI Lab, where we invented State Space Models or SSMs, a new primitive for training efficient, large-scale foundation models. Our team combines deep expertise in model innovation and systems engineering paired with a design-minded product engineering team to build and ship cutting edge models and experiences.

We're funded by leading investors at Index Ventures and Lightspeed Venture Partners, along with Factory, Conviction, A Star, General Catalyst, SV Angel, Databricks and others. We're fortunate to have the support of many amazing advisors, and 90+ angels across many industries, including the world's foremost experts in AI.

The next leap in model intelligence won’t come from scale alone — it will come from better post-training and alignment. Cartesia’s Post-Training team is developing the methods and systems that make multimodal models truly adaptive, aligned, and grounded in human intent.

As a Researcher on the Post-Training team, you’ll work at the intersection of machine learning research, alignment, and infrastructure, designing new techniques for preference optimization, model evaluation, and feedback-driven learning. You’ll explore how feedback signals can guide models to reason more effectively across modalities, and you’ll build the infrastructure to measure and improve these behaviors at scale.

Your work will directly shape how Cartesia’s foundation models learn, improve, and ultimately connect with people.

• Own research initiatives to improve the alignment and capabilities of multimodal models

• Develop new post-training methods and evaluation frameworks to measure model improvement

• Partner closely with research, product, and platform teams to define best practices for creating specialized models

• Implement, debug, and scale experimental systems to ensure reliability and reproducibility across training runs

• Translate research findings into production-ready systems that enhance model reasoning, consistency, and human alignment

• Deep knowledge of preference optimization and alignment methods, including RLHF and related approaches

• Experience designing evaluations and metrics for generative or multimodal models

• Strong engineering and debugging skills, with experience building or scaling complex ML systems

• Ability to trace and diagnose complex behaviors in model performance across the training and evaluation pipeline

• Experience with multimodal model training (e.g., text, audio, or vision-language models)

• Contributions to alignment research or open-source projects related to model evaluation or fine-tuning

• Background in designing or implementing human-in-the-loop evaluation systems

🏢 In-office policy: We’re an in-person team based out of offices in 🇺🇸 San Francisco, 🇬🇧 London and 🇮🇳 Bangalore We love being in the office, hanging out together, and learning from each other every day.