Job Opportunities API

We are rebuilding biotech for the AI era. When a breakthrough is delayed, the world waits. Getting a molecule from discovery to patients, or a crop from lab to field, involves thousands of slow, manual, disconnected steps. AI has the potential to change this, compressing decades of R&D work into years. But that only happens when clean, structured scientific data and AI are built into how science gets done. Benchling is the AI platform for biotech R&D. Scientists use Benchling to design experiments, capture structured data, and run AI agents and models directly in their workflows. Over 200,000 scientists around the world trust Benchling to power their most important work, from academic labs to Sanofi, Moderna, and more than half of the world's top 50 biopharma. We’re building an AI scientist for our customers. We can’t do that if we haven’t built the muscle ourselves. AI fluency is the foundation we build on; it's core to how we work, and we're committed to helping every new hire integrate it into their day-to-day. As part of our interview process, you'll complete a brief AI-focused exercise or discussion so we can understand how you think about and use AI to drive impact in your role. Feel free to reference any tools, platforms, or workflows you use today. ROLE OVERVIEW Benchling's Registry and Inventory offerings provide our scientists with a conceptual and physical representation of the entities that are important to their work, offering a balance of flexibility to facilitate rapid creation of entities and validation when data is finalized, providing tooling for the creation of standardized entity names and identifiers, and supporting materials tracking across the containers and plates in use for their research. They are at the core of our enterprise offering, and they are critical to our key initiatives, including antibody discovery. Our customers interact with these products daily, and they trust them with their most important assets. We are a full-stack team on a mission to accelerate science by creating powerful, intuitive tools that support a broad range of scientific workflows. We obsess over quality and data integrity.   RESPONSIBILITIES - Drive complex, end-to-end projects: Lead the design, implementation, and delivery of high-impact features and systems across the stack (React + Python). Translate product requirements into scalable architecture and thoughtful user experiences, while proactively identifying risks and tradeoffs. - Shape technical direction and architecture: Make foundational engineering decisions that improve system performance, reliability, and scalability. Collaborate with other senior engineers to evolve our platform and influence long-term technical strategy. Collaborate cross-functionally: Work closely with product managers, designers, customer success, and other engineering teams to create clarity and ensure alignment on goals, seamless handoffs, and shared understanding of scientific user needs. - Identify and address technical debt: Own proactive improvements to the codebase and infrastructure. Refactor critical systems for maintainability and performance, and champion investments that improve long-term developer velocity. - Design for performance and scale: Build systems that can handle the complexity and scale of life sciences R&D. Optimize backend performance, frontend responsiveness, and system reliability. - Mentor and support other engineers: Act as a technical mentor and sounding board for other engineers. Help grow the team’s technical skills through guidance, pairing, and knowledge sharing. Be a force multiplier for the team. - Elevate code quality and engineering practices: Set high standards for code quality, testing, documentation, and operational excellence. Lead by example, perform thorough code reviews, and help raise the bar across the team. - Embrace ambiguity and domain complexity: Operate effectively in a fast-pac