Staff AI Engineer

G-P helps organizations build global teams in minutes, not months. As part of this mission, we’ve created an indispensable AI agent for HR leaders, G-P Gia™ .

Gia is our AI-powered global HR agent that provides HR compliance guidance instantly. Built on over a decade of global employment and legal expertise and 100,000+ vetted articles, Gia analyzes and generates compliant documents and delivers the answers that HR leaders trust — reducing reliance on outside legal counsel and cutting compliance costs by up to 95%.

About The Position:

GIA helps organizations handle compliance and HR work that is complex, high-stakes and full of edge cases. As our Staff AI Engineer, you’ll set the technical direction for how we build, evaluate and run AI systems, and you’ll stay close to the code while you do it. You’ll write production software, make the architectural calls, and help the engineers around you do their best work.

What You Will Do:

Build

1. Write the code that matters most. Design and build production LLM and NLP systems, data pipelines and ML services. Guide critical core components through every stage, moving seamlessly from initial prototyping to production deployment and continuous oversight.
2. Make the system trustworthy. Our users act on what GIA tells them. Build the evaluation, monitoring and traceability that catch failures like hallucinations, show why the system gave an answer, and keep a human in the loop where it matters.

Lead

1. Set the technical direction. Define the architecture and engineering standards for our AI systems, and make the calls on models, build vs. buy, and the trade-offs between accuracy, cost and speed. Explain your reasoning clearly to engineers and leadership.
2. Turn ambiguous problems into shippable solutions. Compliance and HR work rarely arrives as a clean spec. Work with product, customers and domain experts to decide what is worth solving with AI, frame it well and drive it through delivery.
3. Raise the engineering bar. Lead design reviews and code reviews, write clear design docs, and mentor engineers at every level. Help shape how we hire, onboard and grow our AI engineers.

Multiply

1. Create reusable foundations. Build shared evaluation tooling, deployment pipelines and data practices so every project ships faster and more safely than the last.
2. Bring the outside in. Track developments in LLMs and applied ML, run focused experiments, and turn the promising ones into real product capability rather than prototypes.
3. Communicate across the business. Partner with product, engineering and leadership, and make complex technical trade-offs understandable to non-technical colleagues.

What We Are Looking For:  

Minimum Requirements: 

• 8+ years building and shipping machine learning systems, with a record of technical leadership on production work
• Strong Python and the ability to write clean, maintainable, production-ready code, with ownership of services from first commit through production support
• Deep, hands-on experience with machine learning and NLP, including deep learning and modern LLMs
• Experience designing evaluation frameworks and metrics, and monitoring models after deployment to keep them reliable
• Strong foundations in statistics, probability and linear algebra
• Experience building and running ML services at scale, with Docker and AWS, including serverless
• A history of making architectural and technical decisions that others adopted, and of leading projects without formal authority
• Experience mentoring engineers and improving how a team builds
• Comfort with ambiguous, open-ended problems
• Excellent communication skills with both technical and non-technical audiences
• A Master’s degree in Computer Science, Machine Learning, Statistics, Engineering, Mathematics or a related field, or equivalent practical experience

Preferred Qualifications: 

• Experience using LLMs for non-conversational work such as extraction, classification, structured outputs and reasoning over documents
• Background in compliance, HR tech, regulatory or other sensitive, audit-heavy domains
• Experience with explainability and human-in-the-loop design
• Terraform or other infrastructure as code