Forward Deployed Engineer (Frontier AI)

• Experience in a hands-on, customer-facing technical role, whether as an FDE, Applied AI Engineer, Solutions Architect, ML Engineer or technical founder \n

• A track record of shipping production software, working directly with customers and deploying into their environments \n

• Strong Python skills. Comfortable building, debugging and maintaining production systems \n

• Good understanding of the LLM stack, including inference trade-offs, model serving and fine-tuning workflows \n

• Experience with cloud infrastructure (AWS, Azure or GCP) and deploying models on GPUs \n

• Strong communication skills. Equally comfortable running discovery with a VP and debugging a latency issue with an ML engineer \n

Onsite SF / NY. $220k - $350k + Equity

\n

\n

We’re working with a frontier AI company building some of the world’s most capable models. They’re seeing significant demand from enterprise customers looking to put these models to work and are growing their Forward Deployed Engineering team across San Francisco and New York.

\n

\n

The challenge isn’t getting AI to work in a demo. It’s making it reliable, scalable and genuinely useful inside complex enterprise environments.

\n

\n

As an FDE, you’ll bridge that gap. You’ll work directly with customers to understand their problems, design the right technical approach and build production-grade AI systems around frontier models. This is a hands-on engineering role with real ownership, from initial discovery through to deployment and iteration.

\n

\n

What you’ll do

\n \n

• Work directly with enterprise customers to scope, build and deploy AI systems in their production environments \n

• Write production code alongside customer engineering teams, rather than just advising them \n

• Build agentic systems, tool-use workflows and integrations on top of frontier models \n

• Fine-tune, serve and optimise models for real customer workloads \n

• Lead technical discovery with executives, then work closely with engineers to deliver the solution \n

• Feed what you learn from customer deployments back into product and research \n

\n

\n