Staff Applied AI Engineer - Enterprise AI Solutions

Role overview

Staff-level Applied AI Engineer focused on delivering generative AI and machine learning solutions to enterprise customers. The role bridges cutting-edge AI research with concrete business outcomes, partnering with clients to scope use cases, build production systems, and translate recurring challenges into reusable internal tooling, recipes, and best practices.

Responsibilities

- Partner with enterprise customers end-to-end on GenAI and ML initiatives, from use case discovery and data exploration through model development and deployment - Design and implement modern AI systems such as retrieval-augmented generation, fine-tuning pipelines, prompt-engineering recipes, and agentic workflows - Build augmented datasets and evaluation harnesses to support model reliability, transparency, and stakeholder trust - Cultivate relationships with customer leadership and technical stakeholders to drive successful project delivery - Collaborate with pre-sales solutions and product teams to map customer needs to platform capabilities and surface roadmap gaps - Standardize solutions alongside other applied engineers, contribute to internal tooling, and lead enablement workshops for customers - Travel up to 25% annually

Requirements

- Bachelor's degree in a quantitative field such as Computer Science, Engineering, Mathematics, or Statistics, or equivalent practical experience - 3+ years of customer-facing experience designing and implementing AI/ML solutions - Strong Python proficiency with solid software engineering fundamentals (modular design, testing, profiling, packaging), including modern tooling for type validation, typed data modeling, testing, packaging, API services, serialization, and ML orchestration - Deep expertise across the applied AI stack: classical ML libraries, deep learning frameworks, foundation-model ecosystems, vector/embedding stores, large-scale data processing, retrieval/RAG platforms, synthetic dataset curation, evaluation workflows, and LLM orchestration or agent-authoring frameworks - Experience leading strategic customer-facing initiatives and collaborating with business stakeholders to ensure ML solutions translate into measurable business outcomes - Excellent presentation skills with both technical and executive audiences, plus the ability to juggle multiple projects in a fast-paced setting

Nice to have

- Hands-on experience with synthetic data generation, advanced evaluation design, and emerging agent-authoring patterns - Track record of teaching or enablement work, including workshops and stakeholder education

Benefits and work setup

- Hybrid position based in San Francisco, CA or New York, NY - On-target earnings of $230,000–$360,000 USD for Tier 1 (San Francisco Bay Area) locations, with equity included in offers; actual compensation varies by role, level, location, skills, and experience