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.
