Software Engineer, Platform

Cartesia Inc.

Job Overview

Location

*HQ - San Francisco, CA

Job Type

Full-time

Full Job Description

📋 Description

• Design and build low-latency, scalable, and reliable model inference and serving stacks for cutting-edge State Space Models (SSMs) to enable real-time multimodal intelligence.
• Collaborate closely with the research team and product engineers to translate novel AI research into production-grade systems and user-facing applications.
• Develop highly parallel, high-quality data processing and evaluation infrastructure to support the training of foundation models at scale.
• Work extensively with Go and Python on the backend and Next.js on the frontend to build and maintain a robust, high-performance tech stack.
• Architect distributed systems that meet stringent demands for performance, reliability, and observability in a production AI environment.
• Operate with significant autonomy to shape product direction and drive zero-to-one engineering initiatives in ambiguous, rapidly evolving technical domains.
• Write clean, maintainable, and well-documented code while navigating complex monorepos and large-scale codebases.
• Rapidly adapt to new technologies and systems as the team pushes the boundaries of AI model architecture and deployment.
• Contribute to the full lifecycle of AI systems—from initial prototyping and experimentation to deployment, monitoring, and iterative improvement.
• Participate in an in-person team culture based in San Francisco, London, and Bangalore, with daily collaboration and knowledge sharing.
• Ship fast without compromising on quality or design, as all work is novel and operates at the frontier of AI research and engineering.
• Engage in an open and inclusive team environment where resources are provided to ensure every engineer can succeed and grow.
• Work alongside a founding team with deep expertise from the Stanford AI Lab, who pioneered State Space Models and continue to lead innovation in foundational AI architectures.
• Directly impact how next-generation AI models are applied across devices and applications through scalable platform infrastructure.
• Balance high technical standards with rapid execution, contributing to systems that serve as the backbone for real-time generative AI experiences.
• Maintain a strong focus on observability, performance optimization, and system resilience when building infrastructure for large-scale model inference.
• Contribute to the evaluation and benchmarking of model performance using custom-built data pipelines and testing frameworks.
• Help define engineering best practices for a team that is building industry-first AI platforms.
• Support and mentor junior engineers while maintaining a high individual contributor output.
• Stay current with advancements in AI model architectures, distributed systems, and inference optimization techniques to continuously improve platform capabilities.
• Work within a fast-paced environment where speed of execution and innovation are prioritized alongside technical excellence.

🎯 Requirements

• Strong engineering skills, comfortable navigating complex codebases and monorepos
• Experience building large-scale distributed systems with high demands on performance, reliability, and observability
• Comfort with Go and Python on the backend and Next.js for the frontend
• Technical leadership with the ability to execute and deliver zero-to-one results amidst ambiguity
• An eye for craft and writing clean and maintainable code
• [bonus] Background in or experience working with machine learning and generative models

🏖️ Benefits

• Competitive base salary alongside attractive equity package
• Monthly commuter allowance to help with transportation to and from the office
• Flexible PTO—take as much time as needed to recharge
• Daily lunch, dinner, and plenty of snacks provided
• Personalized onboarding gift referred to as "Your own personal Yoshi"

Skills & Technologies

Python

JavaScript

Next.js

Onsite

Degree Required

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Cartesia Inc.

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About Cartesia Inc.

Cartesia Inc. builds real-time multimodal AI infrastructure that converts speech to text, text to speech, and audio to structured data with low latency. Its streaming APIs and edge-optimized models let developers embed voice capabilities into applications for customer support, transcription, accessibility, and IoT devices. The company emphasizes privacy, on-device processing options, and pay-as-you-go pricing.

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