Research Engineer – AI Agents

Jump Trading; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

Jump Trading in Singapore is expanding its engineering-focused group to build next-generation AI tooling for developers and quantitative researchers. You will architect and maintain LLM-powered, agent-based tools that streamline workflows and automate complex tasks.

Collaborate with core platform and infrastructure teams to deliver scalable solutions, apply prompt engineering, ensure observability, and own the product lifecycle from prototyping to deployment.

Qualifications

• 2+ years of professional software engineering or applied AI experience.
• Strong Python expertise and experience with LLM APIs or agent/automation frameworks.
• Solid knowledge of OS, networking, automation, and software engineering fundamentals.
• Strong analytical and problem-solving skills focused on developer experience.
• Excellent communication and interpersonal skills.
• Familiarity with Linux and version control tools.
• Reliable and predictable availability.

Responsibilities

• Architect, build, and maintain LLM-powered and agent-based tools.
• Collaborate with platform and infrastructure teams to identify use cases and deliver scalable solutions.
• Apply best practices in prompt engineering, tool/plugin integration, access control, and observability.
• Take ownership throughout the product lifecycle, from prototyping to deployment, rollout, and documentation.
• Champion modern engineering standards and help drive continuous improvement across Jump’s diverse tech landscape.
• Other duties as assigned or needed.

Skills

2+ years experience

Python

LLM APIs

Agent/Automation

Communication

Linux

Observability

Job description

Jump Trading Group is committed to world class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incenting collaboration and mutual respect. At Jump, research outcomes drive more than superior risk adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems.

We are growing our engineering-focused group dedicated to building next-generation AI tools and automation for developers and quantitative researchers. This team partners closely with platform and infrastructure engineers to shape robust, agent-enabled tooling used by trading and research groups across Jump.

What You’ll Do:

• Architect, build, and maintain LLM-powered and agent-based tools to streamline developer workflows and automate complex tasks
• Collaborate with core platform and infrastructure teams to identify use cases and deliver reliable, scalable solutions
• Apply best practices in prompt engineering, tool/plugin integration, access control, and observability
• Take ownership throughout the product lifecycle, from prototyping to deployment, rollout, and documentation
• Champion modern engineering standards and help drive continuous improvement across Jump’s diverse tech landscape
• Other duties as assigned or needed

Skills You’ll Need:

• 2+ years of professional software engineering or applied AI experience
• Strong proficiency in Python, with hands-on experience integrating LLM APIs, building AI-powered applications, or working with agent/automation frameworks
• Understanding of operating systems, networking, automation, and core software engineering fundamentals
• Strong analytical and problem-solving skills, focused on developer experience and usability
• Excellent communication and interpersonal skills
• Familiarity with Linux and version control tools
• Reliable and predictable availability

Bonus Points

• Strong opinions on best practices in ML research, tooling, and/or infrastructure
• Experience with end-to-end model development, especially in LLMs
• Prior academic publications and/or contributions to open-source AI research