Forward Deployed Engineer

Cartesia•San Francisco, CA

6h•Onsite

About The Position

About Cartesia 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. About the Role We’re hiring a Forward Deployed Engineer to advance our mission of building real-time multimodal intelligence by embedding directly with enterprise customers and delivering agentic voice AI solutions into their production environments. FDEs maximize customer success and revenue by turning Cartesia’s core product into deployed, high-impact solutions.

Requirements

4+ years of experience building and operating production software systems
Strong backend engineering fundamentals and a track record of delivering reliable systems
Experience integrating APIs and distributed services into real-world infrastructure
Comfort operating across cloud and containerized environments (AWS, GCP, Kubernetes)
Experience navigating enterprise constraints: authentication, networking, observability, and security reviews
Ability to take ambiguous problems, define structure, and drive them to resolution
High ownership and bias for action — you move quickly without waiting for perfect specs
Clear, structured communicator comfortable engaging with senior technical stakeholders

Nice To Haves

Experience in forward-deployed, solutions, implementation, or customer-facing engineering roles
Experience deploying AI/ML systems into production environments
Familiarity with real-time systems (voice, streaming APIs, telephony, low-latency systems)
Experience with on-prem or hybrid infrastructure deployments
Startup or founder experience

Responsibilities

Write and ship production-grade code – Design, build, and deploy voice AI systems that power real enterprise workflows.
Own deployments end-to-end – Lead discovery, architecture, implementation, and rollout for strategic customer engagements.
Drive progress in ambiguity – Move projects forward when requirements are incomplete, constraints evolve, or systems fail in unexpected ways.
Deliver across complex infrastructure – Deploy across cloud, VPC, and on-prem environments while navigating security, networking, and compliance requirements.
Unblock critical integrations – Diagnose and resolve integration failures, performance bottlenecks, and deployment issues under real-world constraints.
Drive expansion through building – Identify new use cases and prototype solutions that deepen adoption and increase long-term customer value.
Turn customer signal into platform leverage – Surface recurring patterns to engineering and product teams, influencing roadmap and reusable capabilities.
Build for scale – Transform one-off solutions into repeatable playbooks, templates, and reference architectures.