AI Engineer - Materials

Patsnap's Materials team builds intelligent systems that help materials scientists and engineers search, extract, and reason over materials science and patent data. You will own the agentic layer of our products end-to-end from product development to implementing the observability tooling and feedback loops to improve them continuously in production.

Want to see the platform you'd be representing?

Check out this short overview:

This is an in-office position based in our Singapore office.

Who are we?

Patsnap is a global, pre-IPO company that transforms the way organizations harness their Intellectual Property and Research & Development productivity. Our platform revolutionizes how IP and R&D teams collaborate across the entire innovation lifecycle, using domain-specific AI to accelerate the creation of market-ready products. With over 12,000 customers worldwide, including some of the biggest names in innovation, Patsnap is at the forefront of technological advancement. Our $300M Series E funding round brings our valuation to a $1 billion unicorn status, and we still have a remarkable amount of growth ahead.

We have a vibrant and diverse team with offices in Singapore, Toronto, London, Shanghai and remote teams based in US. Our hyper-growth trajectory is powered by our people, and we are extremely proud of our company-wide vision, work ethic, and entrepreneurial spirit. We are committed to fostering an inclusive environment where talent thrives and ideas bloom.

What You'll Be Doing

Design, build, and productionize agentic systems that beat general-purpose AI agents in our domain.

Develop MCP servers (tool design, description and schema) that improve the performance of other agents on tasks in our domain.

Build evaluation frameworks with domain experts to measure answer quality, retrieval performance and scientific accuracy.

Own production reliability & observability of agents you develop from instrumentation to model/provider evals.

Why This Role

Full ownership of a production agent stack that customers pay for

Your evals help decide the roadmap: we build where we can measurably beat frontier general agents

Small senior team, direct access to domain experts and real R&D users

Requirements

Qualifications

Bachelor's degree in engineering, computer science or any field where analytical and critical thinking was demonstrated.

5+ years of software or ML engineering, including 1+ years building and maintaining production LLM-based systems.

Designed evaluations for LLM or agent systems - eval sets, quality metrics, human-expert and/or LLM-judge pipelines.

You have instrumented, monitored, and debugged live AI services (e.g., OpenTelemetry, Arize Phoenix, Langfuse, Datadog, or similar).

Strong software engineering and system design fundamentals.

Nice-to-haves

API/SDK product or MCP (Model Context Protocol) server design experience.

Materials science, chemistry, or patent/IP domain exposure.