Job Purpose:
The Institute of Foundation Models (IFM) is seeking a Developer Experience Engineer (AI/LLMs) to shape, validate, and optimize the end-to-end integration journeys for our frontier foundation models. Operating as an independent, cross-functional bridge between our core research labs, infrastructure teams, and communities, you will own the user-readiness and deployment validation of our portfolio.
Your core mission begins once model checkpoints and initial documentation are drafted. You will act as the ultimate “first customer” rigorously testing every single deployment path, running installation configurations from scratch, and validating that our advanced models’ interface seamlessly with popular AI development frameworks. You will bridge the lab-to-market gap by systematically removing technical bottlenecks, verifying token efficiency, and ensuring that complex model weights translate into robust, friction-free developer workflows.
The ideal candidate is a hands-on, developer-focused engineer who sits at the intersection of AI models, runtime systems, and open-source tooling, with a passion for building bulletproof, intuitive developer experiences.
Key Responsibilities:
Developer Experience & Model Enablement:
- Systematically test and validate every deployment path outlined in our technical documentation across diverse target environments (e.g., Hugging Face, vLLM, TensorRT-LLM, Ollama) to ensure zero friction.
- Audit and optimize the “Zero-to-Hero” onboarding experience, verifying that an external engineer with no internal IFM context can successfully run inference, API queries, or fine-tuning workflows within 15 minutes.
- Identify, reproduce, and document gaps in configuration files, dependency scripts, and setup environments before public or partner release.
- Own the definitive technical sign-off on model release readiness, serving as the objective gatekeeper who ensures the deployment experience matches our world-class research standards.
Technical Integrations & Developer Tooling:
- Build and maintain robust integrations with widely used open-source ecosystems, multi-agent frameworks, and model-serving frameworks to create smooth implementation layers.
- Develop practical, production-grade quickstarts, boilerplate repositories, and reference implementations that demonstrate core model capabilities (e.g., advanced RAG setups, tool-calling).
- Collaborate hands-on with APIs and SDKs to ensure stability, backward compatibility, and predictable error-handling for downstream developers.
- Evaluate emerging AI tooling standards and frameworks, identifying strategic integration opportunities to expand how developers interact with IFM models.
Cross-Functional Bridge & Partner Support:
- Act as the primary technical interface linking internal research scientists, infrastructure/HPC teams, and DevRel/community managers.
- Translate ambiguous community bugs and external enterprise friction points into actionable, high-priority engineering requirements for the core technical teams.
- Partner directly with strategic external clients to stress-test deployment paths on specialized architectures, isolating runtime bottlenecks and latency issues.
- Ensure structural continuity in developer interfaces across parallel product lines, verifying that text architectures and World Models share cohesive integration standards.
Execution, Tracking & Reporting
- Establish clear, transparent mechanisms to track validation pipelines, regression testing logs, and outstanding release issues across concurrent model versions.
- Prepare concise status updates for leadership, highlighting systemic release risks, integration blockers, and final milestone decisions.
- Proactively drive cross-team blockers to resolution by pulling in the correct technical owners and troubleshooting dependencies rather than escalating every issue.
- Refine and simplify deployment playbooks, driving process improvements that streamline how models transition from internal experimentation to external distribution.
Qualifications and Criteria:
- Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or a related quantitative technical field preferred.
Essential:
- Over 5 years of experience in software engineering, applied machine learning, MLOps, or technical project management within a product-building or deep-tech environment.
- Proven track record of managing cross-functional technical projects end-to-end, with clear ownership of scope, timelines, and deployment verification.
- Strong technical competency across terminal environments, Python programming, and modern AI application loops, with the ability to comfortably debug deployment scripts and read error logs.
- Excellent organizational and analytical skills, with a natural capacity to systematically map out, execute, and track complex, multi-variable testing scenarios.
- Clear, structured communicator, highly capable of distilling highly technical software or infrastructure bugs into crisp, actionable status summaries for diverse internal stakeholders and leadership.
- Demonstrated ability to work in flat, fast-moving teams, maintaining complete objective ownership of quality control standards under tight release timelines.
Preferred:
- Direct experience in frontier AI research lab environments, especially working closely with foundation model delivery frameworks.
- Familiarity with the open-source AI landscape, including Hugging Face core repositories and localized inference runtimes.
- Exposure to product development lifecycles, moving complex systems from early exploration stages through to formal public launch.
