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Principal AI Architect

Hop · San Francisco, CA

Extracted from the posting

Base
Not stated
Remote
In-office min 4 days/week, SF SoMa
Funding
Series A-funded
Visa
No visa sponsorship; US work authorization required
Stack
Python, Docker, SQL, PyTorch (company stack)

Principal AI Architect at Draup, defining architecture for agent systems, reasoning, and proprietary model development in a small senior Silicon Valley engineering team. Work covers LLM fine-tuning, knowledge graphs, data modeling, and inference infrastructure.

Draup is a Series A-funded agentic AI company building the intelligence layer for how global enterprises make workforce and go-to-market decisions. We work with 250+ enterprise clients — including 5 of the Fortune 10 — processing 1B+ job descriptions, 850M+ professional profiles, and signals from 100+ labor databases.


We are now building our Silicon Valley engineering team — a small, senior group focused on next-generation AI research and product.


Location: San Francisco, SoMa — minimum 4 days per week in-office.


What you'll do


• Define and own the architectural vision for next-generation AI systems: novel agent architectures, reasoning systems, and proprietary model development.

• Prototype and evaluate breakthrough AI capabilities — predictive modeling, autonomous signal synthesis, multi-modal intelligence — that create defensible IP.

• Set the technical foundations: data architecture, model serving, agent orchestration, and inference infrastructure for a multi-year product horizon.

• Evaluate and adopt emerging AI paradigms (multi-agent reasoning, RLHF, retrieval-augmented fine-tuning) before they are mainstream.

• Establish engineering standards, architecture review processes, and IP documentation practices for the team.


What we require


• BS/MS/PhD in Computer Science, AI/ML, or related field. PhD or equivalent research depth preferred.

• 5+ years in AI/ML engineering with at least 2 years in a principal engineer or lead architect role.

• Demonstrated history of building original AI systems that became products — not implementations of existing patterns.

• Deep expertise across: LLM training and fine-tuning, agentic system design, knowledge graph construction, large-scale data modeling.

• Comfort operating in greenfield conditions: high ambiguity, high ownership.

• Patents, published research, or shipped products with clear proprietary differentiation are strong signals.

• No visa sponsorship. Must be authorized to work in the US without current or future employer sponsorship.

Company stack

This posting doesn’t list technologies. These are the ones Hop uses across its other roles.

PythonDockerSQLPyTorchJavaJiraKubernetesSpring Boot
Hop
Hop

Job details

TypeFull-time
Work ModeOn-site
LevelLead
Location🇺🇸San Francisco, CA
PostedNov 06, 2026
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