채용
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.
ROLE OVERVIEW:
At Benchling, our mission is to empower scientists to accelerate discoveries that change the world. As a Software Engineer on the Developer Platform team, you’ll help build the platform that powers our growing ecosystem of scientific applications. You’ll work on systems that shape how developers, partners, and scientists interact with Benchling, enabling extensibility, integration, and innovation across the platform.
This team works on a wide range of early- and mid-stage initiatives, including developer APIs, event delivery, AI-powered agents, and platform services that support a growing ecosystem. Because of the breadth of the work, engineers have the opportunity to contribute across many parts of our developer-facing platform and to take on projects that align with both business needs and their own interests and strengths.
You’ll collaborate closely with product managers, designers, and engineers across the company to solve challenging problems and build a more scalable, extensible foundation for scientific R&D. Your work will help drive innovation, customization, and seamless integration of third-party solutions that empower scientists to work more efficiently and make groundbreaking discoveries.
RESPONSIBILITIES:
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Contribute to projects across the software development lifecycle, from early exploration and prototyping through implementation and launch
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Build and improve APIs, services, and interfaces that integrate with Benchling’s growing ecosystem
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Partner with cross-functional teams to understand user needs and turn them into scalable, thoughtful software solutions
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Help evolve our platform by sharing ideas, learning quickly, and contributing to technical decisions
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Write clean, maintainable code and support the reliability and usability of the systems you work on
Check out our engineering blog https://benchling.engineering/ for some examples of past work across Benchling. Here are some examples of recent and upcoming projects from our team:
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Event delivery systems for real-time scientific data
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AI-powered agents and platform infrastructure for life sciences
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Developer Interfaces & Platform APIs
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Design, build, and maintain external developer-facing APIs and SDKs that power extensibility in Benchling’s platform.
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Contribute to evolving and scaling our REST and GraphQL APIs, ensuring consistency, performance, and usability for external and internal developers.
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Bonus: AI Agents & Model Context Protocol (MCP)
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Contribute to the development of APIs and interfaces that support AI-driven agents like Benchling’s Deep Research, Ask, and Compose Agents.
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Support the development of our Benchling MCP to enable AI agents to interact safely with Benchling’s data and APIs.
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Collaborate with our AI infra / agents teams to support a comprehensive agentic ecosystem.
QUALIFICATIONS:
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1+ years of professional software engineering experience
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A prior software engineering internship
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Strong abilities in problem solving and iterating on feedback.
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A desire to have ownership over building key pieces of product and/or platform.
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Build software with a product-first approach. You ship code quickly and care about the real world impact of your code.
HOW WE WORK:
We offer a flexible hybrid work arrangement that prioritizes in-office collaboration. Employees are expected to be on-site 3 days per week (Monday, Tuesday, and Thursday).
#BI-Hybrid
Benchling welcomes everyone.
We believe diversity enriches our team so we hire people with a wide range of identities, backgrounds, and experiences.
We are an equal opportunity employer. That means we don’t discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We also consider for employment qualified applicants with arrest and conviction records, consistent with applicable federal, state and local law, including but not limited to the San Francisco Fair Chance Ordinance.
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Benchling 소개

Benchling
Series F+Benchling provides a cloud-based platform for life sciences research and development, offering tools for data management, laboratory workflows, and bioinformatics. The company serves biotechnology and pharmaceutical organizations to accelerate their R&D processes.
501-1,000
직원 수
San Francisco
본사 위치
$6.1B
기업 가치
연봉 정보
51개 데이터
Mid/L4
Senior/L5
Mid/L4 · Engagement Manager
3개 리포트
$207,000
총 연봉
기본급
$180,000
주식
-
보너스
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$172,500
$207,000
뉴스 & 버즈
Benchling Launches AI Connectors to Deepen Integration Between R&D Data and Enterprise AI Tools - TipRanks
TipRanks
News
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3d ago
Benchling Launches AI Connectors to Power the Data Ecosystem for R&D - PR Newswire
PR Newswire
News
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3d ago
[1 YoE, Application Scientist, Field Application Scientist, France]
Hello everyone, I need some feedback for my resume, I'm a PhD in mol/cell biology (3years) with a first exeprience as application scientist (fixed term contract, unfortunately, due to budget restriction and logistic issues they couldn't keep me) I want to find a similar job or a field application scientist (so visiting customers, training them, troubleshooting their protocols etc..), but so far no luck. I have been networking a ton, with lots of FAS telling me they got their first FAS job right
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3w ago
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6
Does Benchling actually generate real value, or is it just overhead?
I’ve been using Benchling in an industry R&D setting, and I’m trying to figure out whether it’s genuinely high ROI or just something teams feel obligated to use. In theory, it should act as a central system for experiment tracking, data organization, and collaboration. But in practice, I’ve seen a few issues: * A lot of manual data entry with unclear downstream use * Data stored but rarely reused or queried * Limited integration with analysis pipelines (R/Python) * Scientists treating it a
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3w ago
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