채용
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Excel
Biotechnology is rewriting life as we know it, from the medicines we take, to the crops we grow, the materials we wear, and the household goods that we rely on every day. But moving at the new speed of science requires better technology.
Benchling’s mission is to unlock the power of biotechnology. The world’s most innovative biotech companies use Benchling’s R&D Cloud to power the development of breakthrough products and accelerate time to milestone and market.
Come help us bring modern software to modern science.
ROLE OVERVIEW:
Benchling is on a mission to accelerate the pace of life sciences research and development. To do that, we build a suite of tools that scientists use daily to solve humanity's most pressing problems. The tools we build must be optimized to make scientists more efficient in their work, across a broad spectrum of research, development, and manufacturing tasks. This is where the Data Input team comes in.
The Data Input team builds a platform and user experiences to help scientists and lab operators more efficiently capture and work with their data. The team develops structured tables, forms, and data entry tooling that enables scientists to input, transform, and propagate their laboratory data downstream. We focus on both the user experience of scientists working in the lab as well as the ability of other software developers to integrate the components we create into their applications. Increasingly, our work involves developing AI-powered features that make data entry faster, more accurate, and more intelligent.
The Data Input team owns components that over 90% of Benchling customers use, and is a key contributor to initiatives with company-wide impact. The team is currently focused on enhancing the core capabilities of the platform that Benchling's most important applications are built on — investing in foundational re-architecture, performance, and scalability work that will unlock new APIs, AI-powered features, and future product capabilities. This is technically complex work that requires careful sequencing and an eye toward long-term payoff. This role is ideal for managers who are energized by deep technical work, who have a track record of leading teams through large-scale refactors and platform migrations, and who find satisfaction in building the foundations that directly enable customer-facing wins.
RESPONSIBILITIES:
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Recruit, develop, and lead a team of exceptional software engineers.
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Partner closely with product and design to define roadmap and long-term product strategy. In the near term, drive execution of foundational technical initiatives and API delivery that improve the performance, reliability, scalability, and extensibility of the team's platform, including identifying and prioritizing opportunities for AI-powered features as the platform matures.
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Hold an unreasonably high bar for product quality as the team executes its work.
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Partner closely with engineers on the team to ensure excellent technical decisions as you lay the foundations for new products and extend mature ones. Directly perform code reviews and engage with the team's codebase.
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Meet with customers to understand their science and what they need from Benchling, particularly around data entry workflows in research, development, and manufacturing contexts.
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Translate strategy into execution by developing and executing quarterly plans with clear metrics and targets.
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Contribute to engineering-wide organizational improvements as part of the management team.
QUALIFICATIONS:
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2+ years of experience as an engineering manager.
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5+ years of experience as a software engineer, with experience as a technical lead on projects that involve multiple engineers across multiple quarters.
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Experience building cloud-based web applications, ideally on both the front- and back-end.
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Experience leading teams through technically complex, long-running platform or infrastructure projects — such as large-scale refactors, backend migrations, or re-architecting core systems — where correctness, performance, and scalability matter more than speed of feature delivery.
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Grown high-trajectory, early-to-mid-career engineers into leaders with company-wide impact.
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Experience (and enjoy!) working directly with customers to inform your team's roadmap.
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A track record of working with cross-functional partners in product, design, sales, and marketing.
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Comfort with and enthusiasm for hands-on technical leadership: you should be excited to write and review code, and view reviewing your engineers' code as an essential part of the job.
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Interest in and ability to develop AI-powered product features, including integrating LLMs and AI capabilities into scientific software workflows.
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A track record of working with cross-functional partners in product, design, sales, and marketing.
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Familiarity with software engineering management productivity tools (e.g., Jira or other bug database), as well as observability tools for logging, metrics, and alerting.
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Interest in / curiosity about biotech! Prior experience with biotech, pharmaceutical, or other life sciences business sectors or education is not required, but is absolutely a plus.
NICE-TO-HAVE
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Frontend development experience, especially with React and TypeScript.
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Python programming experience.
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Experience building both customer-facing features and developer platform / reusable component libraries.
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Experience designing and delivering customer-facing APIs, particularly in a product where the API must coexist with a rich UI.
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Experience with performance optimization, particularly moving computation from client to server and managing the performance implications of that transition.
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Experience in enterprise B2B SaaS, particularly in regulated industries.
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Experience solving challenging performance problems for large-scale web applications.
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
-
$172,500
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뉴스 & 버즈
Benchling Launches AI Connectors to Deepen Integration Between R&D Data and Enterprise AI Tools - TipRanks
TipRanks
News
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1w ago
Benchling Launches AI Connectors to Power the Data Ecosystem for R&D - PR Newswire
PR Newswire
News
·
1w 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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4w 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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4w ago
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2