Cartesia
Forward Deployed Engineer
About this role
Cartesia seeks a Forward Deployed Engineer to embed with enterprise customers and build production voice AI solutions using their state-of-the-art multimodal models. You'll own end-to-end deployments across complex infrastructure while driving customer adoption and informing product strategy.
What you'll do
- Design, build, and deploy production-grade voice AI systems for enterprise customers
- Lead full deployment lifecycle from discovery and architecture through implementation and rollout
- Diagnose and resolve integration failures, performance issues, and deployment challenges in real-world environments
- Navigate enterprise infrastructure constraints including security, networking, compliance, and authentication requirements
- Identify expansion opportunities and prototype new use cases to deepen customer adoption
- Translate customer patterns into reusable playbooks and platform improvements for broader impact
What they're looking for
- Backend software engineering and production system reliability
- API integration and distributed systems deployment
- Cloud and containerized environments (AWS, GCP, Kubernetes)
- Real-time systems and low-latency architecture
- Enterprise infrastructure and compliance navigation
- Problem-solving in ambiguous, evolving situations
- Technical communication with senior stakeholders
- AI/ML production deployment experience
Benefits
- In-person collaboration at offices in San Francisco, London, or Bangalore
- Visa sponsorship support on case-by-case basis
- Opportunity to work on cutting-edge AI research and novel model architectures
- Fast-paced startup environment with high execution standards
- Direct influence on product roadmap through customer insights
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Cartesia
Cartesia builds multimodal AI models and voice AI platforms that power enterprise applications. The company is hiring for roles spanning customer support, enterprise deployments, inference infrastructure, internal developer tooling, and forward-deployed engineering to scale their AI solutions across production environments.
- Website
- cartesia.ai
Likely interview questions
- Walk us through a time you deployed a production system into a complex enterprise environment. What were the biggest obstacles, and how did you navigate them?
- Describe your experience integrating third-party APIs or distributed services. How do you approach debugging integration failures under tight timelines?