About the Role
You will design, build, and scale the AI platform that empowers Gusto's internal teams to build intelligent agents and deliver customer value. This includes developing core platform capabilities such as agent orchestration, RAG infrastructure, eval tooling, model serving, prompt management, observability, and safety guardrails, enabling product teams to rapidly build, test, and ship AI-powered experiences. You will own the full lifecycle from problem framing to production deployment, ensuring the platform is reliable, performant, and easy to adopt. As a Staff MLE, you will also shape technical standards, drive architectural decisions, and mentor engineers across the organization.
What you'll do day-to-day
- Design and build scalable platform services (agent orchestration, RAG pipelines, eval frameworks, model serving) that internal teams use to ship AI-powered products.
- Lead technical strategy and architecture for the AI platform, including model lifecycle, observability, safety guardrails, and evaluation infrastructure.
- Collaborate cross-functionally with app teams, PMs, and Designers to understand their AI needs and deliver platform capabilities that unblock them.
- Build, harden, and operate shared infrastructure that powers intelligent agents across Gusto (e.g., retrieval, routing, prompt management, content selection).
- Stay current with AI/ML research; rapidly prototype and productionize new techniques that strengthen the platform.
- Establish robust practices for validation, deployment, monitoring, and ongoing performance management of platform services and the agents built on them.
- Communicate technical strategy, tradeoffs, and impact clearly to executives and non-technical partners.
- Mentor engineers across teams, raising the bar on AI/ML best practices and fostering a culture of pragmatic innovation.
Requirements
- 12+ years building and deploying end-to-end AI/ML systems, with experience designing platform-level infrastructure that other engineering teams build on.
- Deep expertise in one or more areas: LLMs, NLP, retrieval/RAG, agent orchestration, deep learning, or reinforcement learning.
- Hands-on experience building LLM-based applications and agentic workflows — including prompt engineering, retrieval design, evaluation, and production deployment.
- Proficiency with modern ML frameworks (PyTorch, TensorFlow, Hugging Face) and cloud platforms (GCP, AWS, or Azure).
- Strong Python skills and sound software engineering fundamentals — testing, code review, CI/CD, reliability, and API design.
- Proven track record shipping impactful AI/ML projects to production, ideally in a platform or infrastructure context.
- Demonstrated ability to lead cross-functionally, influence technical direction, and communicate clearly with both engineers and non-technical stakeholders.
- Ph.D. or Master's in CS, ML, Statistics, Mathematics, or related field is a plus.