Benchling
Software Engineer, Agents
About this role
Benchling seeks a Software Engineer to build end-to-end AI agents that automate scientific workflows in biotech R&D. You'll design and ship agent systems spanning experiment design, data analysis, and reporting, working directly with customers while contributing to the platform's LLM infrastructure and user interfaces.
What you'll do
- Build and productionize end-to-end AI agents that automate scientific tasks from experiment design to data analysis and reporting
- Collaborate directly with customers to identify use cases, gather feedback, and evaluate agent performance
- Develop across the full stack, including LLM-powered backends and intuitive frontend interfaces within Benchling applications
- Enhance the agent platform by contributing frameworks, tooling, and infrastructure for future development
- Drive technical experimentation and shape AI development practices as the field evolves
- Rapidly iterate based on customer feedback and market feedback in a fast-paced environment
What they're looking for
- Full-stack software engineering (backend with Python or similar, frontend with React or equivalent)
- LLM and AI agent architecture design and implementation
- Production system development and maintenance
- Product sense and rapid iteration based on user feedback
- Collaborative problem-solving across engineering, product, and domain experts
- Ability to learn biotech concepts quickly without prior domain knowledge
- API integration and data pipeline design
- Customer discovery and feedback gathering
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Benchling
Benchling builds an AI-powered platform for biotech R&D that integrates scientific workflows and data processes to accelerate research breakthroughs. The company is hiring software engineers across full-stack, customer engineering, agentic AI, and security roles to enhance developer productivity, build production AI systems, and protect sensitive research data.
- Website
- benchling.com
Likely interview questions
- Describe a complex system you've built end-to-end from architecture to shipping. How did you approach balancing technical depth with user needs?
- Tell us about your experience working with LLMs or AI agents. What patterns have you found effective, and what challenges have you encountered?