Cartesia
AI

Machine Learning Engineer, Data

Cartesia · San Francisco, CA, US · $180k - $250k

Actively hiring Posted 5 months ago

Role overview

  • Design and build large-scale datasets for model training.
  • Build evaluations of speech models, both via manual annotation and at scale with automated metrics.
  • Implement techniques for steering data generation to improve model intelligence through data and mitigate bias.
  • Build automated quality control systems to validate and filter generated data
  • Partner with product teams to ensure support for key languages and markets.
  • Experience building or working with large multilingual datasets
  • Experience with generative models (speech, text, or multimodal).
  • Ability to help guide human annotation and evaluation across multiple languages.
  • Strong applied ML background with a focus on data-centric approaches.
  • Excitement for building scalable systems that bridge research and production.

Benefits

Lunch, dinner and snacks at the office.

Fully covered medical, dental, and vision insurance for employees.

401(k).

Relocation and immigration support.

Your own personal Yoshi.

Our Culture

We’re an in-person team based out of San Francisco. We love being in the office, hanging out together, and learning from each other every day.

We ship fast. All of our work is novel and cutting edge, and execution speed is paramount. We have a high bar, and we don’t sacrifice quality or design along the way.

We support each other. We have an open & inclusive culture that’s focused on giving everyone the resources they need to succeed.

Compensation Range: $180K - $250K

About the company

Our mission is to build the next generation of AI: ubiquitous, interactive intelligence that runs wherever you are. Today, not even the best models can continuously process and reason over a year-long stream of audio, video and text—1B text tokens, 10B audio tokens and 1T video tokens—let alone do this on-device.

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.

Tags & focus areas

Used for matching and alerts on DevFound
Fulltime Ai Machine Learning
Common Questions

Frequently asked questions

Quick answers about how DevFound's AI matching, resumes, and referrals work.

DevFound's AI Copilot ingests your profile, goals, and live job data to deliver curated matches in seconds. Every match includes a resume variant, suggested referrals, and interview prep so you can act immediately. The more feedback you provide, the sharper the Copilot becomes.

AI-led job searches shrink the hours spent sifting through boards and formatting resumes. DevFound pairs automation with your personal outreach, so you reserve energy for interviews and negotiation. Traditional networking still matters, but AI gives you a lift before you even send a message.

Modern AI roles expect comfort with production-grade code, data fluency, and practical ML tooling. The strongest candidates pair deep technical chops with storytelling—translating model impact to product, GTM, and exec partners. Continuous learning keeps you ahead as stacks evolve.

DevFound rewards active seekers. Keep your profile fresh, respond to match quality prompts, and enable alerts so you never miss a role. The AI prioritizes companies and teams that align with your feedback, accelerating both introductions and interview invites.

High-density tech hubs continue to host the deepest AI talent pools, yet distributed teams are catching up fast. Use DevFound filters to hone in on onsite, hybrid, or fully remote roles and watch openings expand across time zones.

DevFound aggregates thousands of remote AI openings and flags the nuances—core hours, async culture, and visa needs—up front. The Copilot also recommends how to position your distributed work experience so hiring managers know you can thrive on a remote team.