Engineering lead

Patsnap; Singapore; On-site; SGD 180,000 - 240,000; Full time

Job summary

Patsnap is seeking a hands-on engineering leader to own the Materials team's technical roadmap and to strengthen data and search foundations. You will build and guide a multidisciplinary engineering organization spanning data, AI and software engineering, and drive production excellence in reliability, performance and cost.

You will partner with product managers and materials experts to translate customer problems into technical plans and measurable outcomes, shaping the future of knowledge

Qualifications

• Experience directly managing engineers through multiple cycles of growth.
• Own technical roadmaps for complex, data-intensive products.
• Strong background in data pipelines, indexing and search/retrieval.
• Architectural judgement across backend services and distributed systems.
• Hands-on production engineering with modern languages and tooling.

Responsibilities

• Own technical strategy and roadmap across data, search and AI.
• Lead data ingestion, parsing, normalisation and knowledge modelling.
• Build and grow a multidisciplinary engineering team.
• Set standards for reliability, performance and cost.
• Collaborate with product managers to translate problems into plans.

Skills

Engineering management

Technical roadmap ownership

Data and search expertise

Backend & distributed systems

Production engineering

Java/Go/Scala/Python/Rust/C++

Job description

Materials R&D runs on information that is buried in papers, patents, datasheets and specialist terminology. PatSnap’s Materials team is building the data and AI products that make that knowledge searchable, structured and decision-ready for R&D teams.

We're looking for a hands‑on engineering leader to own our technical roadmap, strengthen our data and search foundations, and build the engineering team behind them.

What you'll own

• Technical strategy and roadmap: Assess existing systems, identify architectural gaps and define a coherent technical roadmap across data, search and AI. Prioritise investments and make decisions about what to improve, simplify, replace or rebuild.
• Data and search engineering: Structure and drive work across ingestion, parsing, normalisation, knowledge modelling, indexing and retrieval. Establish clear ownership, milestones and measurable standards for data quality, coverage, freshness and search relevance.
• Engineering organisation: Build, grow and manage a multidisciplinary team spanning data, AI and software engineering. Own hiring, performance management and career development. Establish effective ways of working and develop technical leaders.
• Production excellence: Set engineering standards for system design, testing, deployment and observability. Drive improvements in reliability, performance and cost, including the evaluation and productionisation of AI applications.
• Product collaboration: Partner with product managers and materials experts to translate customer and functional problems into technical plans, engineering priorities and measurable outcomes.

What you bring

• Proven engineering management: Experience directly managing engineers through multiple cycles of team building, hiring, growth and organisational development, including coaching and performance management.
• Technical roadmap ownership: A track record independently defining and executing technical roadmaps for complex, data-intensive products or platforms, from diagnosing problems and evaluating approaches through to delivery.
• Data and search expertise: Substantial experience building or leading production data platforms, pipelines, search or retrieval systems, with a strong understanding of data quality, indexing and relevance.
• Strong engineering fundamentals: Sound architectural judgement across backend services, databases, distributed systems and APIs, with practical experience operating reliable production systems.
• Hands‑on technical credibility: Strong production engineering experience in backend, data or search systems, with proficiency in relevant languages such as Java, Go, Scala, Python, Rust or C++. Comfortable reviewing architecture and code, challenging technical decisions and resolving complex engineering problems.
• Product-oriented leadership: Able to turn ambiguous requirements into actionable engineering plans, make pragmatic trade-offs and communicate clearly across disciplines.

Nice to have

• Search relevance, hybrid retrieval, RAG and LLM-powered applications.
• Document intelligence, OCR and structured information extraction.
• Ontologies, entity resolution and knowledge graphs.
• Scientific or technical data experience, particularly materials science, chemistry or patents.