Developer Experience Engineer

Role overview

Model enablement and integrations

• Test deployment paths across environments such as Hugging Face, vLLM, TensorRT-LLM, and Ollama; validate onboarding for inference, API queries, and fine-tuning.
• Find and document configuration, dependency, and setup gaps; provide technical sign-off on model release readiness.
• Build integrations with open-source, multi-agent, and model-serving frameworks; develop quickstarts, boilerplate repositories, and reference implementations.
• Work with APIs and SDKs to support stability, backward compatibility, and predictable error handling; evaluate emerging AI tooling standards.

Cross-functional delivery and tracking

• Connect research scientists, infrastructure and HPC teams, and developer-relations or community managers; translate external bugs and friction into engineering requirements.
• Partner with external clients to stress-test specialized deployments and isolate runtime bottlenecks and latency issues.
• Track validation pipelines, regression logs, and release issues; report risks and milestones, drive blockers to resolution, and improve deployment playbooks.
• Maintain consistent developer interfaces across product lines, including text architectures and World Models.

Essential experience and capabilities

• Over five years of experience in software engineering, applied machine learning, MLOps, or technical project management in a product-building or deep-tech environment.
• End-to-end cross-functional project ownership, including scope, timelines, and deployment verification.
• Strong terminal, Python, and AI application workflow competency; able to debug deployment scripts and interpret error logs.
• Excellent organization, analytical testing, structured communication, and objective quality ownership in fast-moving teams and tight release timelines.

Education and preferred background

• A bachelor’s degree in Computer Science, Software Engineering, Information Systems, or a related quantitative technical field is preferred.
• Preferred: frontier AI research lab or foundation-model delivery experience; familiarity with open-source AI, Hugging Face repositories, or localized inference runtimes.
• Preferred: exposure to product development from early exploration through formal public launch.